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Wang K, Ren S, Jia Y, Yan X, Wang L, Fan Y. Neuromorphic chips for biomedical engineering. MECHANOBIOLOGY IN MEDICINE 2025; 3:100133. [PMID: 40519866 PMCID: PMC12166701 DOI: 10.1016/j.mbm.2025.100133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2025] [Revised: 04/28/2025] [Accepted: 05/07/2025] [Indexed: 06/18/2025]
Abstract
The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.
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Affiliation(s)
- Kaiyang Wang
- Medical Engineering &Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, 311115, Hangzhou, China
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Shuhui Ren
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, China
| | - Yunfang Jia
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, China
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Jiaruiyuan Biochip Research Center of Hebei University, College of Electron and Information Engineering, Hebei University, Baoding, 071002, China
| | - Lizhen Wang
- Medical Engineering &Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, 311115, Hangzhou, China
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yubo Fan
- Medical Engineering &Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, 311115, Hangzhou, China
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological and Medical Engineering, Beihang University, Beijing, 100191, China
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2
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Liang X, Su D, Tang Y, Xi B, Yang C, Xiu H, Wang J, Liu C, Wang M, Chai Y. Lab-on-device investigation of phase transition in MoO x semiconductors. Nat Commun 2025; 16:4784. [PMID: 40404662 PMCID: PMC12098734 DOI: 10.1038/s41467-025-60050-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 05/12/2025] [Indexed: 05/24/2025] Open
Abstract
Precise tuning of phase transition material properties enables multifunctional devices for information processing and energy conversion, but controlling on-device phase transitions and monitoring microscopic mechanisms remains challenging. Here, we develop a lab-on-device system for molybdenum oxide to probe operando hydrogenation mechanisms through in situ electrical and spectral characterization with density functional theory calculations, revealing threshold-driven proton dynamics that govern the transition between nonvolatile memory operation and catalytic hydrogen evolution. Moderate proton intercalation (flux < 1017 cm-2) achieves a five-order conductance modulation under ambient conditions via polaron formation and stoichiometric optimization (H/Mo up to 22%, Mo/O approaching ideal ratios), outperforming oxygen vacancy engineering. Beyond this threshold (flux ~1017 cm-2), intensive proton intercalation triggers electric-to-chemical energy conversion, directly linking proton history to catalytic activity. Leveraging these principles, we achieve nonvolatile electrochemical memory with linear synaptic and accumulative neuronal functionalities, and demonstrate an all electrochemical random-access memory neural network hardware that executes memory-efficient rank-order coding for sparse signals even under noisy conditions.
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Affiliation(s)
- Xiaoci Liang
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information technology, Sun Yat-Sen University, Guangzhou, China
| | - Dongyue Su
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information technology, Sun Yat-Sen University, Guangzhou, China
| | - Younian Tang
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Bin Xi
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Chunzhen Yang
- School of Materials, Sun Yat-Sen University, Shenzhen, China
| | - Huixin Xiu
- School of Materials Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jialiang Wang
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Chuan Liu
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information technology, Sun Yat-Sen University, Guangzhou, China.
| | - Mengye Wang
- School of Materials, Sun Yat-Sen University, Shenzhen, China.
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
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3
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Du Y, Yang L, Gong J, Hu J, Liu J, Zhang S, Qu S, Chen J, Lee HS, Xu W. A Monolithic Neuromorphic Device for In-Sensor Tactile Computing. J Phys Chem Lett 2025:5312-5320. [PMID: 40393949 DOI: 10.1021/acs.jpclett.5c00583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2025]
Abstract
To emulate the tactile perception of human skin, the integration of tactile sensors with neuromorphic devices has emerged as a promising approach to achieve near-sensor information processing. Here, we present a monolithic electronic device that seamlessly integrates tactile perception and neuromorphic computing functionalities within a single architecture, with synaptic plasticity directly tunable by tactile inputs. This unique capability stems from our engineered device structure employing SnO2 nanowires as the conductive channel coupled with a pressure-sensitive chitosan layer ionic gating layer. The device demonstrates pressure-dependent memory retention and learning behaviors, effectively mimicking the enhanced cognitive functions observed in humans under stressful conditions. Furthermore, the integrated design exhibits potential for implementing bioinspired electronic systems requiring adaptive tactile information processing.
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Affiliation(s)
- Yi Du
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Lu Yang
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Jiangdong Gong
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
| | - Jiahe Hu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Jiaqi Liu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Song Zhang
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Shangda Qu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Jiaxin Chen
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Hwa Sung Lee
- Department of Materials Science and Chemical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan 15588, Republic of Korea
| | - Wentao Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin,College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
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4
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Mei T, Chen F, Huang T, Feng Z, Wan T, Han Z, Li Z, Hu L, Lin CH, Lu Y, Cheng W, Qi DC, Chu D. Ion-Electron Interactions in 2D Nanomaterials-Based Artificial Synapses for Neuromorphic Applications. ACS NANO 2025; 19:17140-17172. [PMID: 40297996 DOI: 10.1021/acsnano.5c02397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2025]
Abstract
With the increasing limitations of conventional computing techniques, particularly the von Neumann bottleneck, the brain's seamless integration of memory and processing through synapses offers a valuable model for technological innovation. Inspired by biological synapse facilitating adaptive, low-power computation by modulating signal transmission via ionic conduction, iontronic synaptic devices have emerged as one of the most promising candidates for neuromorphic computing. Meanwhile, the atomic-scale thickness and tunable electronic properties of van der Waals two-dimensional (2D) materials enable the possibility of designing highly integrated, energy-efficient devices that closely replicate synaptic plasticity. This review comprehensively analyzes advancements in iontronic synaptic devices based on 2D materials, focusing on electron-ion interactions in both iontronic transistors and memristors. The challenges of material stability, scalability, and device integration are evaluated, along with potential solutions and future research directions. By highlighting these developments, this review offers insights into the potential of 2D materials in advancing neuromorphic systems.
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Affiliation(s)
- Tingting Mei
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Fandi Chen
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Tianxu Huang
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Zijian Feng
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Tao Wan
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Zhaojun Han
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane 4000, Australia
| | - Zhi Li
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Long Hu
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Chun-Ho Lin
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Yuerui Lu
- School of Engineering, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 0200, Australia
| | - Wenlong Cheng
- School of Biomedical Engineering, University of Sydney, Darlington, NSW 2008, Australia
| | - Dong-Chen Qi
- School of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Dewei Chu
- School of Materials Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
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5
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Song R, Wang P, Zeng H, Zhang S, Wu N, Liu Y, Zhang P, Xue G, Tong J, Li B, Ye H, Liu K, Wang W, Wang L. Nanofluidic Memristive Transition and Synaptic Emulation in Atomically Thin Pores. NANO LETTERS 2025; 25:5646-5655. [PMID: 40155389 DOI: 10.1021/acs.nanolett.4c06297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/01/2025]
Abstract
Ionic transport across nanochannels is the basis of communications in living organisms, enlightening neuromorphic nanofluidic iontronics. Comparing to the angstrom-scale long biological ionic pathways, it remains a great challenge to achieve nanofluidic memristors at such thinnest limit due to the ambiguous electrical model and interaction process. Here, we report atomically thin memristive nanopores in two-dimensional materials by designing optimized ionic conductance to decouple the memristive, ohmic, and capacitive effects. By conducting different charged iontronics, we realize the reconfigurable memristive transition between nonvolatile-bipolar and volatile-unipolar characteristics, which arises from distinct transport processes governed by energy barriers. Notably, we emulate synaptic functions with ultralow energy consumption of ∼0.546 pJ per spike and reproduce biological learning behaviors. The memristive nanopores are similar to the biosystems in angstrom structure, rich iontronic responses, and millisecond-level operating pulse width, matching the biological potential width. This work provides a new paradigm for boosting brain-inspired nanofluidic devices.
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Affiliation(s)
- Ruiyang Song
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Peng Wang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Haiou Zeng
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Shengping Zhang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
| | - Ningran Wu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
| | - Yuancheng Liu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Pan Zhang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Guodong Xue
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
- State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, P. R. China
| | - Junhe Tong
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Bohai Li
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
| | - Hongfei Ye
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Kaihui Liu
- State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, P. R. China
| | - Wei Wang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
| | - Luda Wang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, P. R. China
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6
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Liang Y, Li H, Tang H, Zhang C, Men D, Mayer D. Bioinspired Electrolyte-Gated Organic Synaptic Transistors: From Fundamental Requirements to Applications. NANO-MICRO LETTERS 2025; 17:198. [PMID: 40122950 PMCID: PMC11930914 DOI: 10.1007/s40820-025-01708-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2024] [Accepted: 02/19/2025] [Indexed: 03/25/2025]
Abstract
Rapid development of artificial intelligence requires the implementation of hardware systems with bioinspired parallel information processing and presentation and energy efficiency. Electrolyte-gated organic transistors (EGOTs) offer significant advantages as neuromorphic devices due to their ultra-low operation voltages, minimal hardwired connectivity, and similar operation environment as electrophysiology. Meanwhile, ionic-electronic coupling and the relatively low elastic moduli of organic channel materials make EGOTs suitable for interfacing with biology. This review presents an overview of the device architectures based on organic electrochemical transistors and organic field-effect transistors. Furthermore, we review the requirements of low energy consumption and tunable synaptic plasticity of EGOTs in emulating biological synapses and how they are affected by the organic materials, electrolyte, architecture, and operation mechanism. In addition, we summarize the basic operation principle of biological sensory systems and the recent progress of EGOTs as a building block in artificial systems. Finally, the current challenges and future development of the organic neuromorphic devices are discussed.
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Affiliation(s)
- Yuanying Liang
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, People's Republic of China.
| | - Hangyu Li
- Institute of Biological Information Processing, Bioelectronics IBI-3, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Hu Tang
- Guangzhou Liby Group Co., Ltd, Guangzhou, 510370, People's Republic of China
| | - Chunyang Zhang
- Guangzhou National Laboratory, Guangzhou, 510005, People's Republic of China
| | - Dong Men
- Guangzhou National Laboratory, Guangzhou, 510005, People's Republic of China
| | - Dirk Mayer
- Institute of Biological Information Processing, Bioelectronics IBI-3, Forschungszentrum Jülich, 52425, Jülich, Germany.
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7
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Sung MJ, Kim KN, Kim C, Lee HH, Lee SW, Kim S, Seo DG, Zhou H, Lee TW. Organic Artificial Nerves: Neuromorphic Robotics and Bioelectronics. Chem Rev 2025; 125:2625-2664. [PMID: 39983019 DOI: 10.1021/acs.chemrev.4c00571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2025]
Abstract
Neuromorphic electronics are inspired by the human brain's compact, energy-efficient nature and its parallel-processing capabilities. Beyond the brain, the entire human nervous system, with its hierarchical structure, efficiently preprocesses complex sensory information to support high-level neural functions such as perception and memory. Emulating these biological processes, artificial nerve electronics have been developed to replicate the energy-efficient preprocessing observed in human nerves. These systems integrate sensors, artificial neurons, artificial synapses, and actuators to mimic sensory and motor functions, surpassing conventional circuits in sensor-integrated electronics. Organic synaptic transistors (OSTs) are key components in constructing artificial nerves, offering tunable synaptic plasticity for complex sensory processing and the mechanical flexibility required for applications in soft robotics and bioelectronics. Compared to traditional sensor-integrated electronics, early implementations of organic artificial nerves (OANs) incorporating OSTs have demonstrated a higher signal-to-noise ratio, lower power consumption, and simpler circuit designs along with on-device processing capabilities and precise control of actuators and biological limbs, driving progress in neuromorphic robotics and bioelectronics. This paper reviews the materials, device engineering, and system integration of the OAN design, highlights recent advancements in neuromorphic robotics and bioelectronics utilizing the OANs, and discusses current challenges and future research directions.
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Affiliation(s)
- Min-Jun Sung
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Kwan-Nyeong Kim
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Chunghee Kim
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyun-Haeng Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Seung-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Somin Kim
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Huanyu Zhou
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- BK21 PLUS SNU Materials Division for Educating Creative Global Leaders, Seoul National University, Seoul 08826, Republic of Korea
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Engineering Research, Seoul National University, Seoul 08826, Republic of Korea
- Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
- Soft Foundry, Seoul National University, Seoul 08826, Republic of Korea
- SN Display Co. Ltd., Seoul 08826, Republic of Korea
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8
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Zheng T, Xie X, Shi Q, Wu J, Yu C. Self-Powered Artificial Neuron Devices: Towards the All-In-One Perception and Computation System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2025; 37:e2416897. [PMID: 39967364 DOI: 10.1002/adma.202416897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2024] [Revised: 02/07/2025] [Indexed: 02/20/2025]
Abstract
The increasing demand for energy supply in sensing units and the computational efficiency of computation units has prompted researchers to explore novel, integrated technology that offers high efficiency and low energy consumption. Self-powered sensing technology enables environmental perception without external energy sources, while neuromorphic computation provides energy-efficient and high-performance computing capabilities. The integration of self-powered sensing technology and neuromorphic computation presents a promising solution for an all-in-one system. This review examines recent developments and advancements in self-powered artificial neuron devices based on triboelectric, piezoelectric, and photoelectric effects, focusing on their structures, mechanisms, and functions. Furthermore, it compares the electrical characteristics of various types of self-powered artificial neuron devices and discusses effective methods for enhancing their performance. Additionally, this review provides a comprehensive summary of self-powered perception systems, encompassing tactile, visual, and auditory perception systems. Moreover, it elucidates recently integrated systems that combine perception, computing, and actuation units into all-in-one configurations, aspiring to realize closed-loop control. The seamless integration of self-powered sensing and neuromorphic computation holds significant potential for shaping a more intelligent future for humanity.
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Affiliation(s)
- Tong Zheng
- College of Electrical Science and Engineering, Southeast university, Nanjing, 210000, China
| | - Xinkai Xie
- College of Electrical Science and Engineering, Southeast university, Nanjing, 210000, China
| | - Qiongfeng Shi
- College of Electrical Science and Engineering, Southeast university, Nanjing, 210000, China
| | - Jun Wu
- College of Electrical Science and Engineering, Southeast university, Nanjing, 210000, China
| | - Cunjiang Yu
- Department of Electrical and Computer Engineering, Department of Mechanical Science and Engineering, Department of Materials Science and Engineering, Department of Bioengineering, Beckman Institute for Advanced Science and Technology, Materials Research Laboratory, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA
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9
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Talin AA, Meyer J, Li J, Huang M, Schwacke M, Chung HW, Xu L, Fuller EJ, Li Y, Yildiz B. Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities. Chem Rev 2025; 125:1962-2008. [PMID: 39960411 DOI: 10.1021/acs.chemrev.4c00512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2025]
Abstract
Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.
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Affiliation(s)
- A Alec Talin
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Jordan Meyer
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jingxian Li
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Mantao Huang
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Miranda Schwacke
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heejung W Chung
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Longlong Xu
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Elliot J Fuller
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Yiyang Li
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bilge Yildiz
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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10
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Wu H, Feng E, Yin H, Zhang Y, Chen G, Zhu B, Yue X, Zhang H, Liu Q, Xiong L. Biomaterials for neuroengineering: applications and challenges. Regen Biomater 2025; 12:rbae137. [PMID: 40007617 PMCID: PMC11855295 DOI: 10.1093/rb/rbae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2024] [Revised: 10/19/2024] [Accepted: 11/03/2024] [Indexed: 02/27/2025] Open
Abstract
Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.
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Affiliation(s)
- Huanghui Wu
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Enduo Feng
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Huanxin Yin
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Yuxin Zhang
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Guozhong Chen
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Beier Zhu
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Xuezheng Yue
- School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Haiguang Zhang
- Rapid Manufacturing Engineering Center, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200444, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
| | - Qiong Liu
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200438, China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
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11
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Kim SH, Jin DG, Kim JH, Baek D, Kim HJ, Yu HY. Enhancement of the Proton-Electron Coupling Effect by an Ionic Oxide-Based Proton Reservoir for High-Performance Artificial Synaptic Transistors. ACS NANO 2025; 19:535-545. [PMID: 39757520 DOI: 10.1021/acsnano.4c10732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Artificial synapses for neuromorphic computing have been increasingly highlighted, owing to their capacity to emulate brain activity. In particular, solid-state electrolyte-gated electrodes have garnered significant attention because they enable the simultaneous achievement of outstanding synaptic characteristics and mass productivity by adjusting proton migration. However, the inevitable interface traps restrict the protons at the channel-electrolyte interface, resulting in the deterioration of synaptic characteristics. Herein, we propose a solid-state electrolyte-based artificial synaptic device with magnesium oxide (MgO) to achieve outstanding synaptic characteristics in humanlike mechanisms by reducing the interface trap density via dangling bond passivation. In addition, the feasibility of utilizing MgO as a proton reservoir, capable of supplying protons stably and maintaining the proton-electron coupling effect, is demonstrated. With the proton reservoir layer, a significantly greater number of conductance weight states, as well as long-term plasticity over 200 s, is achieved at a low operating power (250 fJ). Furthermore, a pattern recognition simulation is performed based on the synaptic characteristics of the proposed synaptic device, yielding a high pattern recognition accuracy of 94.03%. These results imply the potential for advancing high-performance neuromorphic computing systems.
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Affiliation(s)
- Seung-Hwan Kim
- Center for Semiconductor Technology, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Dong-Gyu Jin
- School of Electrical Engineering, Korea University, Seoul 02841, Korea
| | - Jong-Hyun Kim
- Department of Semiconductor Systems Engineering, Korea University, Seoul 02841, Korea
| | - Daeyoon Baek
- Center for Semiconductor Technology, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- School of Electrical Engineering, Korea University, Seoul 02841, Korea
| | - Hyung-Jun Kim
- Center for Semiconductor Technology, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Division of Nano and Information Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea
| | - Hyun-Yong Yu
- School of Electrical Engineering, Korea University, Seoul 02841, Korea
- Department of Semiconductor Systems Engineering, Korea University, Seoul 02841, Korea
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12
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Zou J, Tang L, He W, Zhang X. High-Entropy Oxides: Pioneering the Future of Multifunctional Materials. ACS NANO 2024; 18:34492-34530. [PMID: 39666001 DOI: 10.1021/acsnano.4c12538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2024]
Abstract
The high-entropy concept affords an effective method to design and construct customized materials with desired characteristics for specific applications. Extending this concept to metal oxides, high-entropy oxides (HEOs) can be fabricated, and the synergistic elemental interactions result in the four core effects, i.e., the high-entropy effect, sluggish-diffusion effect, severe-lattice-distortion effect, and cocktail effect. All these effects greatly enhance the functionalities of this vast material family, surpassing conventional low- and medium-entropy metal oxides. For instance, the high phase stability, excellent electrochemical performance, and fast ionic conductivity make HEOs one of the hot next-generation candidate materials for electrochemical energy conversion and storage devices. Significantly, the extraordinary mechanical, electrical, optical, thermal, and magnetic properties of HEOs are very attractive for applications beyond catalysts and batteries, such as electronic devices, optic equipment, and thermal barrier coatings. This review will overview the entropy-stabilized composition and structure of HEOs, followed by a comprehensive introduction to the electrical, optical, thermal, and magnetic properties. Then, several typical applications, i.e., transistor, memristor, artificial synapse, transparent glass, photodetector, light absorber and emitter, thermal barrier coating, and cooling pigment, are synoptically presented to show the broad application prospect of HEOs. Lastly, the intelligence-guided design and high-throughput screening of HEOs are briefly introduced to point out future development trends, which will become powerful tools to realize the customized design and synthesis of HEOs with optimal composition, structure, and performance for specific applications.
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Affiliation(s)
- Jingyun Zou
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Lei Tang
- Songshan Lake Materials Laboratory, Dongguan 523808, China
| | - Weiwei He
- Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, 210022, China
| | - Xiaohua Zhang
- College of Textiles, Innovation Center for Textile Science and Technology, Donghua University, Shanghai 201620, China
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13
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Du Y, Gong J, Wei H, Yang L, Ni Y, Xu W. Ultrasensitive Flexible Organic Synaptic Transistors Modulated by a Chemically Cross-Linked Solvent-Resistive Ion Composite. J Phys Chem Lett 2024; 15:11139-11147. [PMID: 39480058 DOI: 10.1021/acs.jpclett.4c02522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2024]
Abstract
We demonstrate a flexible organic synaptic transistor (FOST) with an ion-composite electrolyte film resistant to chemical reagents, which uses a three-dimensionally cross-linked polyimide matrix to accommodate a high-concentration ionic liquid. FOST shows versatile synaptic plasticity for classical conditioning, high-pass filtering, and the learning-forgetting process. The device achieves low-energy consumption down to 1.02 femtojoule per synaptic event with an ultrasensitive impulse to presynaptic spike down to 0.5 mV. Moreover, the electrical performance of the device is still stable after 1000 mechanical bending cycles. These results demonstrate that the device can be applied to future flexible neuromorphic electronics.
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Affiliation(s)
- Yi Du
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Jiangdong Gong
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
| | - Huanhuan Wei
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
| | - Lu Yang
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Yao Ni
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
| | - Wentao Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Nankai University, Tianjin 300350, China
- Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
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14
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Yuan Y, Patel RK, Banik S, Reta TB, Bisht RS, Fong DD, Sankaranarayanan SKRS, Ramanathan S. Proton Conducting Neuromorphic Materials and Devices. Chem Rev 2024; 124:9733-9784. [PMID: 39038231 DOI: 10.1021/acs.chemrev.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.
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Affiliation(s)
- Yifan Yuan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Ranjan Kumar Patel
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Suvo Banik
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Tadesse Billo Reta
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Ravindra Singh Bisht
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Dillon D Fong
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Subramanian K R S Sankaranarayanan
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Shriram Ramanathan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
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15
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Li R, Yue Z, Luan H, Dong Y, Chen X, Gu M. Multimodal Artificial Synapses for Neuromorphic Application. RESEARCH (WASHINGTON, D.C.) 2024; 7:0427. [PMID: 39161534 PMCID: PMC11331013 DOI: 10.34133/research.0427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024]
Abstract
The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.
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Affiliation(s)
- Runze Li
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
- Institute of Photonic Chips,
University of Shanghai for Science and Technology, Shanghai 200093, China
- Zhangjiang Laboratory, Pudong, Shanghai 201210, China
| | - Zengji Yue
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Haitao Luan
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yibo Dong
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xi Chen
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Min Gu
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
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16
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Li M, Li M, An JS, An H, Kim DH, Lee YH, Park KK, Kim TW. Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability. ACS APPLIED MATERIALS & INTERFACES 2024; 16:42380-42391. [PMID: 39090057 DOI: 10.1021/acsami.4c04957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).
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Affiliation(s)
- Mingjun Li
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Ming Li
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Jun Seop An
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Haoqun An
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Dae Hun Kim
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Yong Hun Lee
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Kwan Kyu Park
- Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Tae Whan Kim
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
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17
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Guo F, Liu Y, Zhang M, Yu W, Li S, Zhang B, Hu B, Li S, Sun A, Jiang J, Hao L. VO 2/MoO 3 Heterojunctions Artificial Optoelectronic Synapse Devices for Near-Infrared Optical Communication. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310767. [PMID: 38456772 DOI: 10.1002/smll.202310767] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/23/2024] [Indexed: 03/09/2024]
Abstract
Artificial optoelectronic synapses (OES) have attracted extensive attention in brain-inspired information processing and neuromorphic computing. However, OES at near-infrared wavelengths have rarely been reported, seriously limiting the application in modern optical communication. Herein, high-performance near-infrared OES devices based on VO2/MoO3 heterojunctions are presented. The textured MoO3 films are deposited on the sputtered VO2 film by using the glancing-angle deposition technique to form a heterojunction device. Through tuning the oxygen defects in the VO2 film, the fabricated VO2/MoO3 heterojunction exhibits versatile electrical synaptic functions. Benefiting from the highly efficient light harvesting and the unique interface effect, the photonic synaptic characteristics, mainly including the short/long-term plasticity and learning experience behavior are successfully realized at the O (1342 nm) and C (1550 nm) optical communication wavebands. Moreover, a single OES device can output messages accurately by converting light signals of the Morse code to distinct synaptic currents. More importantly, a 3 × 3 artificial OES array is constructed to demonstrate the impressive image perceiving and learning capabilities. This work not only indicates the feasibility of defect and interface engineering in modulating the synaptic plasticity of OES devices, but also provides effective strategies to develop advanced artificial neuromorphic visual systems for next-generation optical communication systems.
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Affiliation(s)
- Fuhai Guo
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Yunjie Liu
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Mingcong Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Weizhuo Yu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Siqi Li
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bo Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bing Hu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Shuangshuang Li
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Ankai Sun
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Jianyu Jiang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Lanzhong Hao
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
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18
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Leng K, Wan Y, Fu Y, Wang L, Wang Q. Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309945. [PMID: 38400705 DOI: 10.1002/smll.202309945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Indexed: 02/25/2024]
Abstract
In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.
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Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yu Wan
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yao Fu
- Department of Materials, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Li Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Qisheng Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
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19
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Oh J, Park S, Lee SH, Kim S, Lee H, Lee C, Hong W, Cha J, Kang M, Jin JH, Im SG, Kim MJ, Choi S. Ultrathin All-Solid-State MoS 2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic-Inorganic Hybrid Film. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308847. [PMID: 38566434 PMCID: PMC11187882 DOI: 10.1002/advs.202308847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/24/2024] [Indexed: 04/04/2024]
Abstract
Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.
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Affiliation(s)
- Jungyeop Oh
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seohak Park
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sang Hun Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials EngineeringSejong University209 Neungdong‐ro, Gwangjin‐guSeoul05006Republic of Korea
| | - Hyeonji Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Woonggi Hong
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Jun‐Hwe Cha
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Mingu Kang
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jun Hyup Jin
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Min Ju Kim
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung‐Yool Choi
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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20
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Song HW, Moon D, Won Y, Cha YK, Yoo J, Park TH, Oh JH. A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices. SCIENCE ADVANCES 2024; 10:eadl2882. [PMID: 38781346 PMCID: PMC11114221 DOI: 10.1126/sciadv.adl2882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.
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Affiliation(s)
- Hyun Woo Song
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Dongseok Moon
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Yousang Won
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Yeon Kyung Cha
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Jin Yoo
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Tai Hyun Park
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Joon Hak Oh
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
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21
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Li Y, Cai W, Tao R, Shuai W, Rao J, Chang C, Lu X, Ning H. Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High- k Oxide Composite Dielectric. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19271-19282. [PMID: 38591357 DOI: 10.1021/acsami.4c02880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.
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Affiliation(s)
- Yushan Li
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Wei Cai
- Jihua Laboratory, Foshan, Guangzhou 528000, China
| | - Ruiqiang Tao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Wentao Shuai
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Jingjing Rao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Cheng Chang
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Xubing Lu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Honglong Ning
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China
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22
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Ghafoor F, Kim H, Ghafoor B, Rehman S, Asghar Khan M, Aziz J, Rabeel M, Faheem Maqsood M, Dastgeer G, Lee MJ, Farooq Khan M, Kim DK. Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing. J Colloid Interface Sci 2024; 659:1-10. [PMID: 38157721 DOI: 10.1016/j.jcis.2023.12.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/29/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/-0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.
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Affiliation(s)
- Faisal Ghafoor
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
| | - Honggyun Kim
- Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Bilal Ghafoor
- PPGE3M, Federal University of Rio Grande do Sul, Porto Alegre /RS, Brazil
| | - Shania Rehman
- Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
| | | | - Jamal Aziz
- Chair of Smart Sensor Systems, University of Wuppertal, Wuppertal, Germany
| | - Muhammad Rabeel
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Faheem Maqsood
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, 05006 Seoul, Republic of Korea
| | - Ghulam Dastgeer
- Department of Physics and Astronomy, Sejong University, Seoul 05006, Korea
| | - Myoung-Jae Lee
- Institute of Conversion Daegu Gyeongbuk Institute of Science and Technology (DGIST)., Daegu 42988, Republic of Korea.
| | - Muhammad Farooq Khan
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.
| | - Deok-Kee Kim
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea; Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
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23
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Bag SP, Lee S, Song J, Kim J. Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review. BIOSENSORS 2024; 14:150. [PMID: 38534257 DOI: 10.3390/bios14030150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.
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Affiliation(s)
- Sankar Prasad Bag
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Suyoung Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jaeyoon Song
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jinsink Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
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24
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Jiang S, Peng L, Li L, Dai Q, Pei M, Wu C, Su J, Gu D, Zhang H, Guo H, Qiu J, Li Y. Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations. J Phys Chem Lett 2024; 15:2301-2310. [PMID: 38386516 DOI: 10.1021/acs.jpclett.4c00284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The brain's function can be dynamically reconfigured through a unified neuron-synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric-electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.
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Affiliation(s)
- Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Lichao Peng
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Longfei Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Qinyong Dai
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Chaoran Wu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jian Su
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Ding Gu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Han Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Huafei Guo
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jianhua Qiu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
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25
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Kwak H, Kim N, Jeon S, Kim S, Woo J. Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing. NANO CONVERGENCE 2024; 11:9. [PMID: 38416323 PMCID: PMC10902254 DOI: 10.1186/s40580-024-00415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024]
Abstract
Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.
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Affiliation(s)
- Hyunjeong Kwak
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Nayeon Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seonuk Jeon
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seyoung Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
| | - Jiyong Woo
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea.
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26
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Yu C, Li S, Pan Z, Liu Y, Wang Y, Zhou S, Gao Z, Tian H, Jiang K, Wang Y, Zhang J. Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor. NANO LETTERS 2024; 24:1620-1628. [PMID: 38277130 DOI: 10.1021/acs.nanolett.3c04193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.
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Affiliation(s)
- Chenglin Yu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Shaorui Li
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhoujie Pan
- XingJian College, Tsinghua University, Beijing 100084, China
| | - Yanming Liu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yongchao Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - Siyi Zhou
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhiting Gao
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - He Tian
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Kaili Jiang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Tsinghua-Foxconn Nanotechnology Research Center, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Yayu Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
| | - Jinsong Zhang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
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27
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Ma H, Fang H, Xie X, Liu Y, Tian H, Chai Y. Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception. NANO-MICRO LETTERS 2024; 16:104. [PMID: 38300424 PMCID: PMC10834395 DOI: 10.1007/s40820-024-01330-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.
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Affiliation(s)
- Hailong Ma
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Huajing Fang
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Xinxing Xie
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Yanming Liu
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - He Tian
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China.
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28
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Sunny MM, Thamankar R. Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO 3 based two-terminal devices - efficient way to control the synaptic amplification. RSC Adv 2024; 14:2518-2528. [PMID: 38226148 PMCID: PMC10788777 DOI: 10.1039/d3ra07757h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024] Open
Abstract
Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.
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Affiliation(s)
- Meenu Maria Sunny
- Department of Physics, Vellore Institute of Technology Vellore TN India
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
| | - R Thamankar
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
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29
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Obaidulla SM, Supina A, Kamal S, Khan Y, Kralj M. van der Waals 2D transition metal dichalcogenide/organic hybridized heterostructures: recent breakthroughs and emerging prospects of the device. NANOSCALE HORIZONS 2023; 9:44-92. [PMID: 37902087 DOI: 10.1039/d3nh00310h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
The near-atomic thickness and organic molecular systems, including organic semiconductors and polymer-enabled hybrid heterostructures, of two-dimensional transition metal dichalcogenides (2D-TMDs) can modulate their optoelectronic and transport properties outstandingly. In this review, the current understanding and mechanism of the most recent and significant breakthrough of novel interlayer exciton emission and its modulation by harnessing the band energy alignment between TMDs and organic semiconductors in a TMD/organic (TMDO) hybrid heterostructure are demonstrated. The review encompasses up-to-date device demonstrations, including field-effect transistors, detectors, phototransistors, and photo-switchable superlattices. An exploration of distinct traits in 2D-TMDs and organic semiconductors delves into the applications of TMDO hybrid heterostructures. This review provides insights into the synthesis of 2D-TMDs and organic layers, covering fabrication techniques and challenges. Band bending and charge transfer via band energy alignment are explored from both structural and molecular orbital perspectives. The progress in emission modulation, including charge transfer, energy transfer, doping, defect healing, and phase engineering, is presented. The recent advancements in 2D-TMDO-based optoelectronic synaptic devices, including various 2D-TMDs and organic materials for neuromorphic applications are discussed. The section assesses their compatibility for synaptic devices, revisits the operating principles, and highlights the recent device demonstrations. Existing challenges and potential solutions are discussed. Finally, the review concludes by outlining the current challenges that span from synthesis intricacies to device applications, and by offering an outlook on the evolving field of emerging TMDO heterostructures.
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Affiliation(s)
- Sk Md Obaidulla
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Department of Condensed Matter and Materials Physics, S. N. Bose National Centre for Basic Sciences, Sector III, Block JD, Salt Lake, Kolkata 700106, India
| | - Antonio Supina
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Chair of Physics, Montanuniversität Leoben, Franz Josef Strasse 18, 8700 Leoben, Austria
| | - Sherif Kamal
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
| | - Yahya Khan
- Department of Physics, Karakoram International university (KIU), Gilgit 15100, Pakistan
| | - Marko Kralj
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Zhang C, Ning J, Wang D, Zhang J, Hao Y. A review on advanced band-structure engineering with dynamic control for nonvolatile memory based 2D transistors. NANOTECHNOLOGY 2023; 35:042001. [PMID: 37524059 DOI: 10.1088/1361-6528/acebf4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
With advancements in information technology, an enormous amount of data is being generated that must be quickly accessible. However, conventional Si memory cells are approaching their physical limits and will be unable to meet the requirements of intense applications in the future. Notably, 2D atomically thin materials have demonstrated multiple novel physical and chemical properties that can be used to investigate next-generation electronic devices and breakthrough physical limits to continue Moore's law. Band structure is an important semiconductor parameter that determines their electrical and optical properties. In particular, 2D materials have highly tunable bandgaps and Fermi levels that can be achieved through band structure engineering methods such as heterostructure, substrate engineering, chemical doping, intercalation, and electrostatic doping. In particular, dynamic control of band structure engineering can be used in recent advancements in 2D devices to realize nonvolatile storage performance. This study examines recent advancements in 2D memory devices that utilize band structure engineering. The operational mechanisms and memory characteristics are described for each band structure engineering method. Band structure engineering provides a platform for developing new structures and realizing superior performance with respect to nonvolatile memory.
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Affiliation(s)
- Chi Zhang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Jing Ning
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Dong Wang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
- Xidian-Wuhu Research Institute, Wuhu 241000, People's Republic of China
| | - Jincheng Zhang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Yue Hao
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
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Tran DM, Son JW, Ju TS, Hwang C, Park BH. Dopamine-Regulated Plasticity in MoO 3 Synaptic Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:49329-49337. [PMID: 37819637 DOI: 10.1021/acsami.3c06866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Field-effect transistor-based biosensors have gained increasing interest due to their reactive surface to external stimuli and the adaptive feedback required for advanced sensing platforms in biohybrid neural interfaces. However, complex probing methods for surface functionalization remain a challenge that limits the industrial implementation of such devices. Herein, a simple, label-free biosensor based on molybdenum oxide (MoO3) with dopamine-regulated plasticity is demonstrated. Dopamine oxidation facilitated locally at the channel surface initiates a charge transfer mechanism between the molecule and the oxide, altering the channel conductance and successfully emulating the tunable synaptic weight by neurotransmitter activity. The oxygen level of the channel is shown to heavily affect the device's electrochemical properties, shifting from a nonreactive metallic characteristic to highly responsive semiconducting behavior. Controllable responsivity is achieved by optimizing the channel's dimension, which allows the devices to operate in wide ranges of dopamine concentration, from 100 nM to sub-mM levels, with excellent selectivity compared with K+, Na+, and Ca2+.
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Affiliation(s)
- Duc Minh Tran
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
| | - Jong Wan Son
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Tae-Seong Ju
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Chanyong Hwang
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Bae Ho Park
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
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Xu H, Shang D, Luo Q, An J, Li Y, Wu S, Yao Z, Zhang W, Xu X, Dou C, Jiang H, Pan L, Zhang X, Wang M, Wang Z, Tang J, Liu Q, Liu M. A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing. Nat Commun 2023; 14:6385. [PMID: 37821427 PMCID: PMC10567726 DOI: 10.1038/s41467-023-42172-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023] Open
Abstract
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.
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Affiliation(s)
- Han Xu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Dashan Shang
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Qing Luo
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junjie An
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Yue Li
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuyu Wu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Yao
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Woyu Zhang
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoxin Xu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chunmeng Dou
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Jiang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Liyang Pan
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Xumeng Zhang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Ming Wang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, Hong Kong
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
| | - Qi Liu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
| | - Ming Liu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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Huang M, Schwacke M, Onen M, Del Alamo J, Li J, Yildiz B. Electrochemical Ionic Synapses: Progress and Perspectives. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205169. [PMID: 36300807 DOI: 10.1002/adma.202205169] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.
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Affiliation(s)
- Mantao Huang
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Miranda Schwacke
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Murat Onen
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jesús Del Alamo
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ju Li
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bilge Yildiz
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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Liu G, Wang W, Guo Z, Jia X, Zhao Z, Zhou Z, Niu J, Duan G, Yan X. Silicon based Bi 0.9La 0.1FeO 3 ferroelectric tunnel junction memristor for convolutional neural network application. NANOSCALE 2023; 15:13009-13017. [PMID: 37485606 DOI: 10.1039/d3nr00510k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.
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Affiliation(s)
- Gongjie Liu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Wei Wang
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenqiang Guo
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaotong Jia
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhen Zhao
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenyu Zhou
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Jiangzhen Niu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Guojun Duan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaobing Yan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
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Ismail M, Rasheed M, Mahata C, Kang M, Kim S. Mimicking biological synapses with a-HfSiO x-based memristor: implications for artificial intelligence and memory applications. NANO CONVERGENCE 2023; 10:33. [PMID: 37428275 PMCID: PMC10333172 DOI: 10.1186/s40580-023-00380-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023]
Abstract
Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiOx-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiOx/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (104 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiOx-based memristors have great potential for use in multilevel memory and neuromorphic computing systems.
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Affiliation(s)
- Muhammad Ismail
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Maria Rasheed
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Chandreswar Mahata
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Myounggon Kang
- Department of Electronics Engineering, Korea National University of Transportation, Chungju- si, 27469, Republic of Korea.
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
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Shi J, Kang S, Feng J, Fan J, Xue S, Cai G, Zhao JS. Evaluating charge-type of polyelectrolyte as dielectric layer in memristor and synapse emulation. NANOSCALE HORIZONS 2023; 8:509-515. [PMID: 36757200 DOI: 10.1039/d2nh00524g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Based on credible advantages, organic materials have received more and more attention in memristor and synapse emulation. In particular, an implementation of the ionic pathway as a dielectric layer is essential for organic materials used as building blocks of memristor and artificial synaptic devices. Herein, we describe an evaluation of the use of positive and negative polyelectrolytes as dielectric layers for a memristor with calcium ion (Ca2+) doping. The device based on a negative polyelectrolyte shows the potential to obtain an excellent resistive switching performance and synapse functionality, especially in the transformation behaviours from short-term plasticity (STP) to long-term plasticity (LTP) in both the potentiation and depression processes, which were comparable to the perfomrmance obtained with a positive polyelectrolyte. The mechanism of electrical resistance transition and synaptic function can be attributed to the migration of the doped Ca2+ and the ionic functional groups of polyelectrolyte, which result in the formation and vanishing filament-like Ca2+ flux.
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Affiliation(s)
- Jingzhou Shi
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Shaohui Kang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jiang Feng
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jiaming Fan
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Song Xue
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Gangri Cai
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jin Shi Zhao
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
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Chen H, Li H, Ma T, Han S, Zhao Q. Biological function simulation in neuromorphic devices: from synapse and neuron to behavior. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2183712. [PMID: 36926202 PMCID: PMC10013381 DOI: 10.1080/14686996.2023.2183712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.
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Affiliation(s)
- Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| | - Huilin Li
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Ting Ma
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Shuangshuang Han
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
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40
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Cheng S, Zhong L, Yin J, Duan H, Xie Q, Luo W, Jie W. Controllable digital and analog resistive switching behavior of 2D layered WSe 2 nanosheets for neuromorphic computing. NANOSCALE 2023; 15:4801-4808. [PMID: 36779310 DOI: 10.1039/d2nr06580k] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe2 nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 104. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe2 nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.
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Affiliation(s)
- Siqi Cheng
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Lun Zhong
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Jinxiang Yin
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Huan Duan
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Qin Xie
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wenbo Luo
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wenjing Jie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
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Shao H, Li Y, Yang W, He X, Wang L, Fu J, Fu M, Ling H, Gkoupidenis P, Yan F, Xie L, Huang W. A Reconfigurable Optoelectronic Synaptic Transistor with Stable Zr-CsPbI 3 Nanocrystals for Visuomorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208497. [PMID: 36620940 DOI: 10.1002/adma.202208497] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/19/2022] [Indexed: 06/17/2023]
Abstract
Reconfigurable phototransistor memory attracts considerable attention for adaptive visuomorphic computing, with highly efficient sensing, memory, and processing functions integrated onto a single device. However, developing reconfigurable phototransistor memory remains a challenge due to the lack of an all-optically controlled transition between short-term plasticity (STP) and long-term plasticity (LTP). Herein, an air-stable Zr-CsPbI3 perovskite nanocrystal (PNC)-based phototransistor memory is designed, which is capable of broadband photoresponses. Benefitting from the different electron capture ability of Zr-CsPbI3 PNCs to 650 and 405 nm light, an artificial synapse and non-volatile memory can be created on-demand and quickly reconfigured within a single device for specific purposes. Owing to the optically reconfigurable and wavelength-aware operation between STP and LTP modes, the integrated blue feature extraction and target recognition can be demonstrated in a homogeneous neuromorphic vision sensor array. This work suggests a new way in developing perovskite optoelectronic transistors for highly efficient in-sensor computing.
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Affiliation(s)
- He Shao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Yueqing Li
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Wei Yang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Xiang He
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Le Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Jingwei Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Mingyang Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Haifeng Ling
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
- Department of Molecular Electronics, Max Planck Institute for Polymer Research, 55128, Mainz, Germany
| | - Paschalis Gkoupidenis
- Department of Molecular Electronics, Max Planck Institute for Polymer Research, 55128, Mainz, Germany
| | - Feng Yan
- Department of Applied Physics, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, P. R. China
| | - Linghai Xie
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
- Frontiers Science Center for Flexible Electronics (FSCFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, 710072, P. R. China
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42
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Zhang F, Li C, Li Z, Dong L, Zhao J. Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. MICROSYSTEMS & NANOENGINEERING 2023; 9:16. [PMID: 36817330 PMCID: PMC9935897 DOI: 10.1038/s41378-023-00487-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.
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Affiliation(s)
- Fanqing Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Chunyang Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Zhongyi Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong, China
| | - Jing Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
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43
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Zhang Q, Li E, Wang Y, Gao C, Wang C, Li L, Geng D, Chen H, Chen W, Hu W. Ultralow-Power Vertical Transistors for Multilevel Decoding Modes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208600. [PMID: 36341511 DOI: 10.1002/adma.202208600] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralow-power vertical transistor is demonstrated based on transition-metal carbides/nitrides (MXene) and organic single crystal. The transistor exhibits a high JON of 16.6 mA cm-2 and a high JON /JOFF ratio of 9.12 × 105 under an ultralow working voltage of -1 mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.
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Affiliation(s)
- Qing Zhang
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Enlong Li
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Yongshuai Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Changsong Gao
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Congyong Wang
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Lin Li
- Institute of Molecular Plus, Tianjin University, Tianjin, 300072, China
| | - Dechao Geng
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Huipeng Chen
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Wei Chen
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Wenping Hu
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
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44
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Wang S, Liu X, Zhou P. The Road for 2D Semiconductors in the Silicon Age. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106886. [PMID: 34741478 DOI: 10.1002/adma.202106886] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.
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Affiliation(s)
- Shuiyuan Wang
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Xiaoxian Liu
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
- Frontier Institute of Chip and System, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, 200433, China
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45
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Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
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Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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46
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Fu Y, Chan YT, Jiang YP, Chang KH, Wu HC, Lai CS, Wang JC. Polarity-Differentiated Dielectric Materials in Monolayer Graphene Charge-Regulated Field-Effect Transistors for an Artificial Reflex Arc and Pain-Modulation System of the Spinal Cord. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202059. [PMID: 35619163 DOI: 10.1002/adma.202202059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/28/2022] [Indexed: 06/15/2023]
Abstract
The nervous system is a vital part of organisms to survive and it endows them with remarkable abilities, such as perception, recognition, regulation, learning, and decision-making, by intertwining myriad neurons. To realize such outstanding efficacies and functions, many artificial devices and systems have been investigated to emulate the operating principles of the nervous system. Here, an artificial reflex arc (ARA) and artificial pain modulation system (APMS) are proposed to imitate the unconscious behaviors of the spinal cord. Gdx Oy - and Alx Oy -based charge-regulated field-effect transistors (CRFETs) with a monolayer graphene channel are fabricated and adopted as inhibitory and excitatory synapses, respectively, under the same pulse signals to mimic the biological reflex arc through a connection with a poly(vinylidene fluoride-co-trifluoroethylene)-based actuator. Additionally, a memristor is integrated with a CRFET as the interneuron to regulate the Dirac point by controlling the voltage drop on the graphene channel, analogous to the descending pain-inhibition system in the spinal cord, to prevent excessive pain perception. The proposed ARA and APMS provide a significant step forward to realizing the functions of the nervous system, giving promising potential for developing future intelligent alarm systems, neuroprosthetics, and neurorobotics.
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Affiliation(s)
- Yi Fu
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Ya-Ting Chan
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Yi-Pei Jiang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
| | - Jer-Chyi Wang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
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47
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Hu X, Liu K, Cai Y, Zang SQ, Zhai T. 2D Oxides for Electronics and Optoelectronics. SMALL SCIENCE 2022. [DOI: 10.1002/smsc.202200008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Xiaozong Hu
- Henan Key Laboratory of Crystalline Molecular Functional Materials Henan International Joint Laboratory of Tumor Theranostical Cluster Materials Green Catalysis Center, and College of Chemistry Zhengzhou University Zhengzhou 450001 P. R. China
| | - Kailang Liu
- State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 P. R. China
| | - Yongqing Cai
- Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Taipa 999078 Macau P. R. China
| | - Shuang-Quan Zang
- Henan Key Laboratory of Crystalline Molecular Functional Materials Henan International Joint Laboratory of Tumor Theranostical Cluster Materials Green Catalysis Center, and College of Chemistry Zhengzhou University Zhengzhou 450001 P. R. China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 P. R. China
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Wu Z, Shi P, Xing R, Xing Y, Ge Y, Wei L, Wang D, Zhao L, Yan S, Chen Y. Quasi-two-dimensional α-molybdenum oxide thin film prepared by magnetron sputtering for neuromorphic computing. RSC Adv 2022; 12:17706-17714. [PMID: 35765332 PMCID: PMC9199084 DOI: 10.1039/d2ra02652j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/09/2022] [Indexed: 11/21/2022] Open
Abstract
Two-dimensional (2D) layered materials have attracted intensive attention in recent years due to their rich physical properties, and shown great promise due to their low power consumption and high integration density in integrated electronics. However, mostly limited to mechanical exfoliation, large scale preparation of the 2D materials for application is still challenging. Herein, quasi-2D α-molybdenum oxide (α-MoO3) thin film with an area larger than 100 cm2 was fabricated by magnetron sputtering, which is compatible with modern semiconductor industry. An all-solid-state synaptic transistor based on this α-MoO3 thin film is designed and fabricated. Interestingly, by proton intercalation/deintercalation, the α-MoO3 channel shows a reversible conductance modulation of about four orders. Several indispensable synaptic behaviors, such as potentiation/depression and short-term/long-term plasticity, are successfully demonstrated in this synaptic device. In addition, multilevel data storage has been achieved. Supervised pattern recognition with high recognition accuracy is demonstrated in a three-layer artificial neural network constructed on this α-MoO3 based synaptic transistor. This work can pave the way for large scale production of the α-MoO3 thin film for practical application in intelligent devices.
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Affiliation(s)
- Zhenfa Wu
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Peng Shi
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Ruofei Xing
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yuzhi Xing
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yufeng Ge
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Lin Wei
- School of Microelectronics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Dong Wang
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Le Zhao
- School of Electronic and Information Engineering, Qilu University of Technology Jinan 250353 China
| | - Shishen Yan
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yanxue Chen
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
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Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors. Proc Natl Acad Sci U S A 2022; 119:e2204852119. [PMID: 35648822 DOI: 10.1073/pnas.2204852119] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SignificanceEnabling distributed neurologic and cognitive functions in soft deformable devices, such as robotics, wearables, skin prosthetics, bioelectronics, etc., represents a massive leap in their development. The results presented here reveal the device characteristics of the building block, i.e., a stretchable elastomeric synaptic transistor, its characteristics under various levels of biaxial strain, and performances of various stretchy distributed neuromorphic devices. The stretchable neuromorphic array of synaptic transistors and the neuromorphic imaging sensory skin enable platforms to create a wide range of soft devices and systems with implemented neuromorphic and cognitive functions, including artificial cognitive skins, wearable neuromorphic computing, artificial organs, neurorobotics, and skin prosthetics.
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Liu F, Deswal S, Christou A, Shojaei Baghini M, Chirila R, Shakthivel D, Chakraborty M, Dahiya R. Printed synaptic transistor-based electronic skin for robots to feel and learn. Sci Robot 2022; 7:eabl7286. [PMID: 35648845 DOI: 10.1126/scirobotics.abl7286] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.
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Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Mahdieh Shojaei Baghini
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Radu Chirila
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Dhayalan Shakthivel
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Moupali Chakraborty
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
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