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Lee Y, Huang Y, Chang YF, Yang SJ, Ignacio ND, Kutagulla S, Mohan S, Kim S, Lee J, Akinwande D, Kim S. Programmable Retention Characteristics in MoS 2-Based Atomristors for Neuromorphic and Reservoir Computing Systems. ACS NANO 2024; 18:14327-14338. [PMID: 38767980 DOI: 10.1021/acsnano.4c00333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.
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Affiliation(s)
- Yoonseok Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Yifu Huang
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Yao-Feng Chang
- Intel Corporation, Hillsboro, Oregon 97124, United States
| | - Sung Jin Yang
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Nicholas D Ignacio
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Shanmukh Kutagulla
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sivasakthya Mohan
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sunghun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
| | - Jungwoo Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
| | - Deji Akinwande
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
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2
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Yang C, Wang H, Cao Z, Chen X, Zhou G, Zhao H, Wu Z, Zhao Y, Sun B. Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2308918. [PMID: 38149504 DOI: 10.1002/smll.202308918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Indexed: 12/28/2023]
Abstract
Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.
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Affiliation(s)
- Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Hongyan Wang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Xiaoliang Chen
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing, 400715, China
| | - Hongbin Zhao
- State Key Laboratory of Advanced Materials for Smart Sensing, General Research Institute for Nonferrous Metals, Beijing, 100088, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Yong Zhao
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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Armendarez NX, Mohamed AS, Dhungel A, Hossain MR, Hasan MS, Najem JS. Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity. ACS APPLIED MATERIALS & INTERFACES 2024; 16:6176-6188. [PMID: 38271202 DOI: 10.1021/acsami.3c16003] [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
Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.
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Affiliation(s)
- Nicholas X Armendarez
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Ahmed S Mohamed
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Anurag Dhungel
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Razuan Hossain
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Sakib Hasan
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Joseph S Najem
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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Im IH, Baek JH, Kim SJ, Kim J, Park SH, Kim JY, Yang JJ, Jang HW. Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307334. [PMID: 37708845 DOI: 10.1002/adma.202307334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/24/2023] [Indexed: 09/16/2023]
Abstract
Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.
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Affiliation(s)
- In Hyuk Im
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Ju Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jaehyun Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sung Hyuk Park
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae Young Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea
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6
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Meng S, Wang N, Cao X. Built-In Piezoelectric Nanogenerators Promote Sustainable and Flexible Supercapacitors: A Review. MATERIALS (BASEL, SWITZERLAND) 2023; 16:6916. [PMID: 37959515 PMCID: PMC10647822 DOI: 10.3390/ma16216916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
Energy storage devices such as supercapacitors (SCs), if equipped with built-in energy harvesters such as piezoelectric nanogenerators, will continuously power wearable electronics and become important enablers of the future Internet of Things. As wearable gadgets become flexible, energy items that can be fabricated with greater compliance will be crucial, and designing them with sustainable and flexible strategies for future use will be important. In this review, flexible supercapacitors designed with built-in nanogenerators, mainly piezoelectric nanogenerators, are discussed in terms of their operational principles, device configuration, and material selection, with a focus on their application in flexible wearable electronics. While the structural design and materials selection are highlighted, the current shortcomings and challenges in the emerging field of nanogenerators that can be integrated into flexible supercapacitors are also discussed to make wearable devices more comfortable and sustainable. We hope this work may provide references, future directions, and new perspectives for the development of electrochemical power sources that can charge themselves by harvesting mechanical energy from the ambient environment.
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Affiliation(s)
- Shuchang Meng
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Ning Wang
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Xia Cao
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
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7
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Ding G, Zhao J, Zhou K, Zheng Q, Han ST, Peng X, Zhou Y. Porous crystalline materials for memories and neuromorphic computing systems. Chem Soc Rev 2023; 52:7071-7136. [PMID: 37755573 DOI: 10.1039/d3cs00259d] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.
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Affiliation(s)
- Guanglong Ding
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - JiYu Zhao
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
- State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials, Dalian University of Technology, Dalian 116024, China
- State Key Laboratory of Fine Chemicals, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Kui Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - Qi Zheng
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Xiaojun Peng
- State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials, Dalian University of Technology, Dalian 116024, China
- State Key Laboratory of Fine Chemicals, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
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8
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Pei M, Zhu Y, Liu S, Cui H, Li Y, Yan Y, Li Y, Wan C, Wan Q. Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO 2 Memcapacitive Synapse Arrays. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2305609. [PMID: 37572299 DOI: 10.1002/adma.202305609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/10/2023] [Indexed: 08/14/2023]
Abstract
Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO2 (HZO) for a power-efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium-oxide-based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof-of-concept, a touchless user interface for virtual shopping based on the OMC-based reservoir computing system is demonstrated, benefiting from its interference-robust acoustic and electrophysiological perception. These results shed light on the development of highly power-efficient human-machine interfaces and machine-learning platforms.
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Affiliation(s)
- Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Ying Zhu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Siyao Liu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Hangyuan Cui
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yating Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yang Yan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, 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, 210093, P. R. China
| | - Changjin Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Qing Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, P. R. China
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9
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Lee J, Jeong BH, Kamaraj E, Kim D, Kim H, Park S, Park HJ. Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system. Nat Commun 2023; 14:5775. [PMID: 37723149 PMCID: PMC10507016 DOI: 10.1038/s41467-023-41419-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/01/2023] [Indexed: 09/20/2023] Open
Abstract
An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.
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Affiliation(s)
- Jongmin Lee
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Bum Ho Jeong
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Eswaran Kamaraj
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea
| | - Dohyung Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Hakjun Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Sanghyuk Park
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea.
| | - Hui Joon Park
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Smart Semiconductor, Seoul, 04763, Republic of Korea.
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10
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Wan T, Shao B, Ma S, Zhou Y, Li Q, Chai Y. In-Sensor Computing: Materials, Devices, and Integration Technologies. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203830. [PMID: 35808962 DOI: 10.1002/adma.202203830] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/10/2022] [Indexed: 06/15/2023]
Abstract
The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.
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Affiliation(s)
- Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bangjie Shao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Sijie Ma
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yue Zhou
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Qiao Li
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
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11
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Patil H, Kim H, Kadam KD, Rehman S, Patil SA, Aziz J, Dongale TD, Ali Sheikh Z, Khalid Rahmani M, Khan MF, Kim DK. Flexible Organic-Inorganic Halide Perovskite-Based Diffusive Memristor for Artificial Nociceptors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:13238-13248. [PMID: 36867070 DOI: 10.1021/acsami.2c16481] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
With the current evolution in the artificial intelligence technology, more biomimetic functions are essential to execute increasingly complicated tasks and respond to challenging work environments. Therefore, an artificial nociceptor plays a significant role in the advancement of humanoid robots. Organic-inorganic halide perovskites (OHPs) have the potential to mimic the biological neurons due to their inherent ion migration. Herein, a versatile and reliable diffusive memristor built on an OHP is reported as an artificial nociceptor. This OHP diffusive memristor showed threshold switching properties with excellent uniformity, forming-free behavior, a high ION/IOFF ratio (104), and bending endurance over >102 cycles. To emulate the biological nociceptor functionalities, four significant characteristics of the artificial nociceptor, such as threshold, no adaptation, relaxation, and sensitization, are demonstrated. Further, the feasibility of OHP nociceptors in artificial intelligence is being investigated by fabricating a thermoreceptor system. These findings suggest a prospective application of an OHP-based diffusive memristor in the future neuromorphic intelligence platform.
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Affiliation(s)
- Harshada Patil
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
| | - Honggyun Kim
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
| | - Kalyani D Kadam
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
- Department of Convergence Engineering for Intelligent Drone, Sejong University, 05006 Seoul, Republic of Korea
| | - Shania Rehman
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
| | - Supriya A Patil
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jamal Aziz
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Zulfqar Ali Sheikh
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
- Department of Convergence Engineering for Intelligent Drone, Sejong University, 05006 Seoul, Republic of Korea
| | - Mehr Khalid Rahmani
- School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
| | - Deok-Kee Kim
- Department of Electrical Engineering, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea
- Department of Convergence Engineering for Intelligent Drone, Sejong University, 05006 Seoul, Republic of Korea
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12
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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: 3.0] [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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