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Sun S, Li YC, Zhu L, Zhang CY, Chen S, Xia YD, Wu D, Li AD. Optoelectronic Artificial Synapses Based on ZnO/Nanoporous Hybrid Thin Film by Atomic/Molecular Layer Deposition. J Phys Chem Lett 2025; 16:4668-4674. [PMID: 40315033 DOI: 10.1021/acs.jpclett.5c00856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2025]
Abstract
An optoelectronic artificial synapse device has been established based on ZnO/Zn-HQ nanoporous hybrid thin films by atomic/molecular layer deposition. This unique nanoporous microstructure in devices significantly enhances the optoelectronic response and relaxation time, enabling important biosynaptic functions under optical stimuli, such as Pavlov's dog experiment and pattern recognition. The mechanism of ionization-deionization of oxygen vacancies has been proposed for this device.
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Affiliation(s)
- Song Sun
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
| | - Yu-Chen Li
- Kuang Yaming Honors School and Institute for Brain Sciences, Nanjing University, Nanjing 210023, P. R. China
| | - Lin Zhu
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
| | - Chu-Yi Zhang
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
| | - Shuang Chen
- Kuang Yaming Honors School and Institute for Brain Sciences, Nanjing University, Nanjing 210023, P. R. China
| | - Yi-Dong Xia
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
| | - Di Wu
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
| | - Ai-Dong Li
- National Laboratory of Solid-State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210093, P.R.China
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Cao Y, Li Y, Zhu G, Li L, Lu G, Lim EG, Liu W, Liu Y, Zhao C, Wen Z. Advances in perovskite-based neuromorphic computing devices. NANOSCALE 2025; 17:12014-12047. [PMID: 40310388 DOI: 10.1039/d5nr00335k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2025]
Abstract
Neuromorphic computing devices, inspired by the architecture and functionality of the human brain, offer a promising solution to the limitations imposed by the von Neumann bottleneck on contemporary computing systems. Perovskite materials are widely used in the photosensitive layer of neuromorphic computing devices due to their high light absorption coefficient and excellent carrier mobility. Here, we summarise the latest research progress on neural morphology computing devices based on perovskite materials with different structures and summarise different application scenarios. Finally, we discussed the issues that still need to be addressed and looked forward to the future development of neural morphology calculations based on perovskite materials.
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Affiliation(s)
- Yixin Cao
- Department of Military Biomedical Engineering, Air Force Medical University, Xi'an 710032, China
| | - Yuanxi Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.
| | - Ganggui Zhu
- Department of Military Biomedical Engineering, Air Force Medical University, Xi'an 710032, China
| | - Linhui Li
- Department of Military Biomedical Engineering, Air Force Medical University, Xi'an 710032, China
| | - Guohua Lu
- Department of Military Biomedical Engineering, Air Force Medical University, Xi'an 710032, China
| | - Eng Gee Lim
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.
| | - Wenqing Liu
- Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
| | - Yina Liu
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.
| | - Chun Zhao
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.
| | - Zhen Wen
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China.
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Hu Y, Lin Y, Zhang X, Zhao Y, Li L, Zhang Y, Lei H, Pan Y. Optoelectronic synapses realized on large-scale continuous MoSe 2 with Te doping induced tunable memory functions. NANOSCALE HORIZONS 2025. [PMID: 40331287 DOI: 10.1039/d5nh00062a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2025]
Abstract
Synaptic devices with integrated sensing-computing-storage functions are emerging as promising technological solutions to break the memory wall in the von Neuman architecture computing system. 2D semiconductors are ideal candidate materials for artificial synapses due to their superior electronic and optoelectronic properties. In this work, we report robust optoelectronic synapses realized on wafer-scale continuous MoSe2 with Te-doping-induced tunable memory functions. A unique defect engineering strategy of substitutional doping of Te has been adopted to induce Se vacancies in chemical vapour deposition grown MoSe2 films. These vacancies introduce defect states as deep trap levels in the band gap, enabling efficient charge trapping and significantly prolonging the decaying time. The presence of Te doping and Se vacancies was confirmed by PL, Raman, and XPS characterization. Ultra-high vacuum stencil lithography technique has been adopted for the fabrication of arrayed optoelectronic devices that exhibit prominent excitatory postsynaptic currents with the paired-pulse facilitation up to 197% under ultraviolet illumination. Therefore, essential synaptic behaviors like the spike-number-, spike-rate-, and spike-intensity-dependent plasticity have been demonstrated, along with the in-sensor computation application of hardware image sharpening capability. This work offers a new method of vacancy engineering in large-scale 2D semiconductors for future application in integrated neuromorphic devices.
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Affiliation(s)
- Yongqi Hu
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Yunan Lin
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Xutao Zhang
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Yanlu Zhao
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Lan Li
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Yinuo Zhang
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Hong Lei
- Shaanxi Institute for Pediatric Diseases, Xi'an Children's Hospital, Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710003, China
| | - Yi Pan
- Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
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Sung J, Cheon HJ, Lee D, Chung S, Ayuningtias L, Yang H, Jeon B, Seo B, Kim YH, Lee E. Improving ion uptake in artificial synapses through facilitated diffusion mechanisms. MATERIALS HORIZONS 2025. [PMID: 40272205 DOI: 10.1039/d5mh00005j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2025]
Abstract
Several studies have explored ways to enhance the interaction between the channel layer and ions to realize artificial synapses using organic electrochemical transistors (OECTs). The attachment of glycol side chains can remarkably enhance the ion transport to improve nonvolatile properties via polar groups; however, a comprehensive and methodical evaluation of this phenomenon has yet to be conducted. In this study, we observed the reactivity toward ions and the doping mechanism that changes by glycol group substitution to the side chains of DPP polymers. The analysis revealed that in the presence of glycol chains, the doping mechanism changes to diffusion-dominated, which allows ions to penetrate the channel and interact with it more intensely, thereby enhancing synaptic performance. The fabricated devices successfully mimicked the behavior of biological synapses, such as good long-term synaptic plasticity (LTP), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D). Based on these properties, a high accuracy of 93.7% has been achieved in an artificial neural network for handwritten data recognition at the Modified national institute of standards and technology (MNIST). These findings provide new insights for the realization of artificial synapses and could inspire other research involving reactions with ions.
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Affiliation(s)
- Junho Sung
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
| | - Hyung Jin Cheon
- Department of Chemistry and RIMA, Gyeongsang National University, Jinju, 52828, Republic of Korea.
| | - Donghwa Lee
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
| | - Sein Chung
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Landep Ayuningtias
- Department of Chemistry and RIMA, Gyeongsang National University, Jinju, 52828, Republic of Korea.
| | - Hoichang Yang
- Department of Chemical Engineering, Inha University, Incheon, 22212, Republic of Korea
| | - Byeongjun Jeon
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
| | - Bumjoon Seo
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
| | - Yun-Hi Kim
- Department of Chemistry and RIMA, Gyeongsang National University, Jinju, 52828, Republic of Korea.
| | - Eunho Lee
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
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Wu H, Lin Z, Liu J, Zhang C, Tan C, Wang Z. Ta 2PdS 6/MoS 2 Heterojunction Phototransistor for High-Performance Photoelectric Synapses and Graphic Identification. ACS APPLIED MATERIALS & INTERFACES 2025; 17:20116-20124. [PMID: 40102202 DOI: 10.1021/acsami.5c01804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2025]
Abstract
Artificial synapses and neurons with efficient, high-speed, and highly parallel information processing capabilities are considered to be a new direction for the next generation of learning, cognition, and data storage. In this work, we have integrated photodetectors and photoelectric synapse in Ta2PdS6/MoS2 van der Waals heterostructures, which can be used in photodetection and optical artificial neural networks. We have systematically studied the photoelectric characteristics of the blue-violet to near-infrared (405 ∼ 1550 nm) band. At 633 nm, the responsivity and specific detectivity are as high as 590.36 AW-1 and 5.63 × 1011 Jones, respectively. In addition, the heterojunction acquired a persistent photoconductivity behavior due to the presence of interfacial defect states, which was used to simulate the synaptic properties of the human brain, such as transition from short-term memory to long-term memory, paired-pulse facilitation, "learning-forgetting-relearning" behavior, and excitatory-postsynaptic current. In addition, on the basis of photoelectric synapse, the recognition of handwritten digits with different noise levels by an artificial neural network was simulated, which shows a high training accuracy (90%). This study lays a foundation for the development of high-performance heterojunctions and artificial synapses using two-dimensional materials.
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Affiliation(s)
- Haijuan Wu
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Zhicheng Lin
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Jinxiu Liu
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Chunchi Zhang
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Chao Tan
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Zegao Wang
- College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China
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Ma X, Zhou Y, Li R, Zhao S, Zhang M. Ultralow Power Optoelectronic Memtransistors Based on Vertical WS 2/In 2Se 3 van der Waals Heterostructures. ACS APPLIED MATERIALS & INTERFACES 2025; 17:18582-18591. [PMID: 40085138 DOI: 10.1021/acsami.4c21946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/16/2025]
Abstract
Memtransistors composed of 2D van der Waals (vdW) heterostructures are crucial for constructing artificial synaptic devices and realizing neuromorphic computing. The functional integration containing ultralow power, nonvolatile memory, and biomimetic synaptic behavior endows such devices with broad prospects. Here, we develop an optoelectronic memtransistor based on the WS2/In2Se3 vdW heterostructure and realize significant optical and electrical synaptic properties, which can simulate both short-range plasticity (STP) and long-range plasticity (LTP) of biological synapses. Under optical stimulation, the device demonstrates an ultralow power consumption (only 7.7 aJ per spike) significantly lower than biological synapses, indicating the application potential in large-scale neuromorphic hardware. Combining optical and electrical stimuli, we can perform multiple logic operations by controlling the optical and electrical inputs of the WS2/In2Se3-based memtransistor. Besides, simulated recognition utilizing the Modified National Institute of Standards and Technology data set can achieve a recognition accuracy of 85.41%. Notably, this accuracy can remain above 80% even with the introduction of Gaussian noise. These results demonstrate the promising potential of WS2/In2Se3-based memtransistors in future neuromorphic computing.
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Affiliation(s)
- Xiudong Ma
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, Shandong 266100, China
| | - Yumeng Zhou
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, Shandong 266100, China
| | - Ru Li
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Shangzhou Zhao
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, Shandong 266100, China
| | - Mingjia Zhang
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, Shandong 266100, China
- Engineering Research Center of Advanced Marine Physical Instruments and Equipment, Ministry of Education, Ocean University of China, Qingdao 266100, China
- Qingdao Key Laboratory of Optics and Optoelectronics, Ocean University of China, Qingdao 266100, China
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Ma M, Huang C, Yang M, He D, Pei Y, Kang Y, Li W, Lei C, Xiao X. Ultra-Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe 2 for Neuromorphic Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2406402. [PMID: 39434458 DOI: 10.1002/smll.202406402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 09/22/2024] [Indexed: 10/23/2024]
Abstract
Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe2) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.
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Affiliation(s)
- Mingjun Ma
- The Institute of Technological Sciences, Wuhan University, Wuhan, Hubei, 430072, China
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Chaoning Huang
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Mingyu Yang
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Dong He
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Yongfeng Pei
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Yufan Kang
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Wenqing Li
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, Hubei, 430072, China
| | - Xiangheng Xiao
- School of Physics and Technology, Hubei Nuclear Solid Physics Key Laboratory, Wuhan University, Wuhan, 430072, China
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Sun Z, Fang L, Shi F, Shi Z, Wei T, Bi W, Wang Y, Yan J. Dual-functional GaN photodiode for photodetection and neuromorphic computing with high specific detectivity. OPTICS LETTERS 2024; 49:6821-6824. [PMID: 39602759 DOI: 10.1364/ol.540307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 11/06/2024] [Indexed: 11/29/2024]
Abstract
The discrepancy in the photoresponse characteristics between photodetectors (PDs) and photosynapses is posing a challenge for integrating the two functions into a single device. In this work, a photodetector integrated with neuromorphic capabilities is demonstrated. Under the joint action of the photovoltaic effect and photoconductive effect, the proposed device can switch between the photodetector and photosynapse by simply altering the polarity of bias voltage. Under a reverse or zero bias voltage, the device operates as a photodetector with low dark current and high specific detectivity. On the other hand, when a positive bias voltage is applied, the device showcases synaptic functionalities such as excitatory postsynaptic current, paired-pulse facilitation, and transition from short-term memory to long-term memory. The dual-functional devices hold promise for weak light detection and neuromorphic computing in complex environments.
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Luo J, Tong X, Yue S, Wu K, Li X, Zhao H, Wang B, Li Z, Liu X, Wang ZM. Tailored Environment-Friendly Reverse Type-I Colloidal Quantum Dots for a Near-Infrared Optical Synapse and Artificial Vision System. ACS NANO 2024; 18:29991-30003. [PMID: 39431329 DOI: 10.1021/acsnano.4c10795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2024]
Abstract
Colloidal quantum dots (QDs) are emerging as potential candidates for constructing near-infrared (NIR) photodetectors (PDs) and artificial optoelectronic synapses due to solution processability and a tunable bandgap. However, most of the current NIR QDs-optoelectronic devices are still fabricated using QDs with incorporated harmful heavy metals of lead (Pb) and mercury (Hg), showing potential health and environment risks. In this work, we tailored eco-friendly reverse type-I ZnSe/InP QDs by copper (Cu) doping and extended the photoresponse from the visible to NIR region. Transient absorption spectroscopy analysis revealed the presence of Cu dopant states in ZnSe/InP:Cu QDs that facilitated the extraction of photogenerated charge carriers, leading to an enhanced photodetection performance. Specifically, under 400 nm illumination, the Cu-doped ZnSe/InP QDs-based PDs presented a broadband photodetection ranging from ultraviolet (UV) to NIR, with a responsivity of 70.5 A W-1 and detectivity of 2.8 × 1011 Jones, surpassing those of the undoped ZnSe/InP QDs-based PDs (49.4 A W-1 and 1.9 × 1011 Jones, respectively). More importantly, the ZnSe/InP:Cu QDs-PDs demonstrated various synapse-like characteristics of short-term plasticity (STP), long-term plasticity (LTP), and learning-forging-relearning under NIR light illumination, which were further used to construct PD array devices for simulating the artificial visual system that is available in prospective optical neuromorphic applications.
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Affiliation(s)
- Jingying Luo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xin Tong
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yunnan Key Laboratory of Electromagnetic Materials and Devices, Yunnan University, Kunming 650091, China
- Shimmer Center, Tianfu Jiangxi Laboratory, Chengdu 641419, China
- Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
| | - Shuai Yue
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China
| | - Keming Wu
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China
| | - Xin Li
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hongyang Zhao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Binyu Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhuojian Li
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xinfeng Liu
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China
| | - Zhiming M Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
- Shimmer Center, Tianfu Jiangxi Laboratory, Chengdu 641419, China
- Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
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Li P, Wang K, Jiang S, He G, Zhang H, Cheng S, Li Q, Zhu Y, Fu C, Wei H, He B, Li Y. Optical Bio-Inspired Synaptic Devices. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1573. [PMID: 39404300 PMCID: PMC11477948 DOI: 10.3390/nano14191573] [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: 08/19/2024] [Revised: 09/19/2024] [Accepted: 09/27/2024] [Indexed: 10/19/2024]
Abstract
The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous "von Neumann storage wall" problem. Inspired by neural synapses, neuromorphic computing has emerged as a promising solution to address the von Neumann problem due to its excellent adaptive learning and parallel capabilities. Notably, in 2016, researchers integrated light into neuromorphic computing, which inspired the extensive exploration of optoelectronic and all-optical synaptic devices. These optical synaptic devices offer obvious advantages over traditional all-electric synaptic devices, including a wider bandwidth and lower latency. This review provides an overview of the research background on optoelectronic and all-optical devices, discusses their implementation principles in different scenarios, presents their application scenarios, and concludes with prospects for future developments.
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Affiliation(s)
- Pengcheng Li
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Kesheng Wang
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Shanshan Jiang
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Gang He
- School of Materials Science and Engineering, Anhui University, Hefei 230601, China
| | - Hainan Zhang
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Shuo Cheng
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Qingxuan Li
- School of Integrated Circuits, Anhui University, Hefei 230601, China
| | - Yixin Zhu
- Yongjiang Laboratory (Y-LAB), Ningbo 315202, China
| | - Can Fu
- School of Materials Science and Engineering, Anhui University, Hefei 230601, China
| | - Huanhuan Wei
- School of Materials Science and Engineering, Anhui University, Hefei 230601, China
| | - Bo He
- School of Materials Science and Engineering, Anhui University, Hefei 230601, China
| | - Yujiao Li
- School of Materials Science and Engineering, Anhui University, Hefei 230601, China
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An J, Zhang N, Tan F, Zhao X, Chang C, Lanza M, Li S. Programmable Optoelectronic Synaptic Transistors Based on MoS 2/Ta 2NiS 5 Heterojunction for Energy-Efficient Neuromorphic Optical Operation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2403103. [PMID: 38778502 DOI: 10.1002/smll.202403103] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/14/2024] [Indexed: 05/25/2024]
Abstract
The optoelectronic synaptic transistors with various functions, broad spectral perception, and low power consumption are an urgent need for the development of advanced optical neural network systems. However, it remains a great challenge to realize the functional diversification of the systems on a single device. 2D van der Waals (vdW) materials can combine unique properties by stacking with each other to form heterojunctions, which may provide a strategy for solving this problem. Herein, an all-2D vdW heterojunction-based programmable optoelectronic synaptic transistor based on MoS2/Ta2NiS5 heterojunctions is demonstrated. The device implements reconfigurable, multilevel non-volatile memory (NVM) states through sequential modulation of multiple optical and electrical stimuli to achieve broadband (532-808 nm), energy-efficient (17.2 fJ), hetero-synaptic functionality in a bionic manner. The intrinsic working mechanisms of the photogating effect caused by band alignment and the interfacial trapping defect modulation induced by gate voltage are revealed by Kelvin-probe force microscopy (KPFM) measurements and carrier transport analysis. Overall, the (opto)electronic synaptic weight controllability for combined in-sensor and in-memory logic processors is realized by the heterojunction properties. The proposed findings facilitate the technical realization of generic all 2D hetero-synapses for future artificial vision systems, opto-logical systems, and Internet of Things (IoT) entities.
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Affiliation(s)
- Junru An
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
- School of Materials Science and Engineering, Hainan University, No. 58 Renmin Road, Haikou, 570228, P. R. China
| | - Nan Zhang
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
| | - Fan Tan
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
| | - Xingyu Zhao
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
| | - Chunlu Chang
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
| | - Mario Lanza
- Materials Science and Engineering Program, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Shaojuan Li
- State Key Laboratory of Luminescence Science and Technology, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, P. R. China
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12
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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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13
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Li Y, He G, Wang W, Fu C, Jiang S, Fortunato E, Martins R. A high-performance organic lithium salt-doped OFET with the optical radical effect for photoelectric pulse synaptic simulation and neuromorphic memory learning. MATERIALS HORIZONS 2024; 11:3867-3877. [PMID: 38787754 DOI: 10.1039/d4mh00297k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Simulation of synaptic characteristics is essential for the application of organic field effect transistors (OFETs) in neural morphology. Although excellent performance, including bias stability and mobility, as well as photoelectric pulse synaptic simulation, has been achieved in SiO2-gated OFETs with PDVT-10 as an organic channel, there are relatively few studies on photoelectric pulse synaptic simulation of electrolyte-gated OFETs based on environmentally friendly and low-voltage operation. Herein, synaptic transistors based on organic semiconductors are reported to simulate the photoelectric pulse response by developing solution-based organic semiconductor PDVT-10, and polyvinyl alcohol (PVA) with an electric double layer (EDL) effect to act as a channel and gate dielectric layer, respectively, and organic lithium salt-doped PVA is used to enhance the EDL effect. The presence of electrical pulses in doped devices not only achieves basic electrical synaptic characteristics, but also significantly realizes the long-term characteristics, pain perception, memory and sensitization applications. Furthermore, the introduction of photoinitiator molecules into the channel layer leads to improved photosynaptic performances by using light-induced free radicals, and the photoelectric synergistic effect has been actualized by introducing heterojunction architecture. This work provides promising prospects for achieving photoelectric pulse modulation based on organic synaptic devices, which shows great potential for the development of artificial intelligence.
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Affiliation(s)
- Yujiao Li
- Field Effect Device & Flexible Display Lab, School of Materials Science and Engineering, Anhui University, Hefei 230601, P. R. China.
| | - Gang He
- Field Effect Device & Flexible Display Lab, School of Materials Science and Engineering, Anhui University, Hefei 230601, P. R. China.
| | - Wenhao Wang
- Field Effect Device & Flexible Display Lab, School of Materials Science and Engineering, Anhui University, Hefei 230601, P. R. China.
| | - Can Fu
- Field Effect Device & Flexible Display Lab, School of Materials Science and Engineering, Anhui University, Hefei 230601, P. R. China.
| | - Shanshan Jiang
- School of Integrated Circuits, Anhui University, Hefei 230601, P. R. China
| | - Elvira Fortunato
- Department of Materials Science/CENIMAT-I3N, Faculty of Sciences and Technology, New University of Lisbon and CEMOP-UNINOVA Campus de Caparica 2829-516 Caparica, Portugal
| | - Rodrigo Martins
- Department of Materials Science/CENIMAT-I3N, Faculty of Sciences and Technology, New University of Lisbon and CEMOP-UNINOVA Campus de Caparica 2829-516 Caparica, Portugal
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14
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Lee CW, Yoo C, Han SS, Song YJ, Kim SJ, Kim JH, Jung Y. Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility. ACS NANO 2024; 18:18635-18649. [PMID: 38950148 DOI: 10.1021/acsnano.4c04851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.
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Affiliation(s)
- Chung Won Lee
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Changhyeon Yoo
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Sang Sub Han
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Yu-Jin Song
- Department of Materials Science and Engineering, Dong-A University, Saha-Gu, Busan, 49315, Republic of Korea
| | - Seung Ju Kim
- The Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Jung Han Kim
- Department of Materials Science and Engineering, Dong-A University, Saha-Gu, Busan, 49315, Republic of Korea
| | - Yeonwoong Jung
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Materials Science and Engineering and Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
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15
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Meng Y, Cheng G. Human somatosensory systems based on sensor-memory-integrated technology. NANOSCALE 2024; 16:11928-11958. [PMID: 38847091 DOI: 10.1039/d3nr06521a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.
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Affiliation(s)
- Yanfang Meng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
| | - Guanggui Cheng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
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16
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Sun Y, Wang H, Xie D. Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications. NANO-MICRO LETTERS 2024; 16:211. [PMID: 38842588 PMCID: PMC11156833 DOI: 10.1007/s40820-024-01445-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024]
Abstract
Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.
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Affiliation(s)
- Yilin Sun
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
| | - Huaipeng Wang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - Dan Xie
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
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Lai J, Shi K, Qiu B, Liang J, Liu H, Zhang W, Yu G. Spacer Engineering Enables Fine-Tuned Thin Film Microstructure and Efficient Charge Transport for Ultrasensitive 2D Perovskite-Based Heterojunction Phototransistors and Optoelectronic Synapses. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310002. [PMID: 38109068 DOI: 10.1002/smll.202310002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Indexed: 12/19/2023]
Abstract
2D Ruddlesden-Popper phase layered perovskites (RPLPs) hold great promise for optoelectronic applications. In this study, a series of high-performance heterojunction phototransistors (HPTs) based on RPLPs with different organic spacer cations (namely butylammonium (BA+), cyclohexylammonium (CyHA+), phenethylammonium (PEA+), p-fluorophenylethylammonium (p-F-PEA+), and 2-thiophenethylammonium (2-ThEA+)) are fabricated successfully, in which high-mobility organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b]benzothiophene is adopted to form type II heterojunction channels with RPLPs. The 2-ThEA+-RPLP-based HPTs show the highest photosensitivity of 3.18 × 107 and the best detectivity of 9.00 × 1018 Jones, while the p-F-PEA+-RPLP-based ones exhibit the highest photoresponsivity of 5.51 × 106 A W-1 and external quantum efficiency of 1.32 × 109%, all of which are among the highest reported values to date. These heterojunction systems also mimicked several optically controllable fundamental characteristics of biological synapses, including excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory states. The device based on 2-ThEA+-RPLP film shows an ultra-high PPF index of 234%. Moreover, spacer engineering brought fine-tuned thin film microstructures and efficient charge transport/transfer, which contributes to the superior photodetection performance and synaptic functions of these RPLP-based HPTs. In-depth structure-property correlations between the organic spacer cations/RPLPs and thin film microstructure/device performance are systematically investigated.
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Affiliation(s)
- Jing Lai
- Key Laboratory of Solid-State Optoelectronic Devices of Zhejiang Province, College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, P. R. China
| | - Keli Shi
- Key Laboratory of Solid-State Optoelectronic Devices of Zhejiang Province, College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, P. R. China
| | - Beibei Qiu
- Key Laboratory of Solid-State Optoelectronic Devices of Zhejiang Province, College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, P. R. China
| | - Jufang Liang
- Key Laboratory of Solid-State Optoelectronic Devices of Zhejiang Province, College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, P. R. China
| | - Haicui Liu
- Key Laboratory of Solid-State Optoelectronic Devices of Zhejiang Province, College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321004, P. R. China
| | - Weifeng Zhang
- Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, P. R China
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Gui Yu
- Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, P. R China
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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18
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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19
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Liu D, Zhang J, Shi Q, Sun T, Xu Y, Li L, Tian L, Xiong L, Zhang J, Huang J. Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305370. [PMID: 37506027 DOI: 10.1002/adma.202305370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/15/2023] [Indexed: 07/30/2023]
Abstract
For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.
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Affiliation(s)
- Dapeng Liu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Qianqian Shi
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Tian
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - 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 Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - Jianhua Zhang
- Key Laboratory of Advanced Display and System Application, Ministry of Education, Shanghai University, Shanghai, 200072, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
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20
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Guo Z, Zhang J, Yang B, Li L, Liu X, Xu Y, Wu Y, Guo P, Sun T, Dai S, Liang H, Wang J, Zou Y, Xiong L, Huang J. Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2310155. [PMID: 38100140 DOI: 10.1002/adma.202310155] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.
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Affiliation(s)
- Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ben Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Shilei Dai
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Haixia Liang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jun Wang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yidong Zou
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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21
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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22
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Wang Y, Yin L, Huang S, Xiao R, Zhang Y, Li D, Pi X, Yang D. Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision. NANO LETTERS 2023; 23:8460-8467. [PMID: 37721358 DOI: 10.1021/acs.nanolett.3c01853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.
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Affiliation(s)
- Yue Wang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Lei Yin
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Shijie Huang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Rulei Xiao
- National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
| | - Yiqiang Zhang
- School of Materials Science and Engineering & College of Chemistry, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Dongke Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Deren Yang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
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23
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Vats G, Hodges B, Ferguson AJ, Wheeler LM, Blackburn JL. Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205459. [PMID: 36120918 DOI: 10.1002/adma.202205459] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Metal halide perovskite based materials have emerged over the past few decades as remarkable solution-processable optoelectronic materials with many intriguing properties and potential applications. These emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier-diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, recent progress toward memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality is reviewed.
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Affiliation(s)
- Gaurav Vats
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
- Department of Physics and Astronomy, Katholieke Universiteit Leuven, Celestijnenlaan 200D, Leuven, B-3001, Belgium
| | - Brett Hodges
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | | | - Lance M Wheeler
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
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24
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Tripathy DB, Gupta A. Nanomembranes-Affiliated Water Remediation: Chronology, Properties, Classification, Challenges and Future Prospects. MEMBRANES 2023; 13:713. [PMID: 37623773 PMCID: PMC10456521 DOI: 10.3390/membranes13080713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023]
Abstract
Water contamination has become a global crisis, affecting millions of people worldwide and causing diseases and illnesses, including cholera, typhoid, and hepatitis A. Conventional water remediation methods have several challenges, including their inability to remove emerging contaminants and their high cost and environmental impact. Nanomembranes offer a promising solution to these challenges. Nanomembranes are thin, selectively permeable membranes that can remove contaminants from water based on size, charge, and other properties. They offer several advantages over conventional methods, including their ability to remove evolving pollutants, low functioning price, and reduced ecological influence. However, there are numerous limitations linked with the applications of nanomembranes in water remediation, including fouling and scaling, cost-effectiveness, and potential environmental impact. Researchers are working to reduce the cost of nanomembranes through the development of more cost-effective manufacturing methods and the use of alternative materials such as graphene. Additionally, there are concerns about the release of nanomaterials into the environment during the manufacturing and disposal of the membranes, and further research is needed to understand their potential impact. Despite these challenges, nanomembranes offer a promising solution for the global water crisis and could have a significant impact on public health and the environment. The current article delivers an overview on the exploitation of various engineered nanoscale substances, encompassing the carbonaceous nanomaterials, metallic, metal oxide and metal-organic frameworks, polymeric nano-adsorbents and nanomembranes, for water remediation. The article emphasizes the mechanisms involved in adsorption and nanomembrane filtration. Additionally, the authors aim to deliver an all-inclusive review on the chronology, technical execution, challenges, restrictions, reusability, and future prospects of these nanomaterials.
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Affiliation(s)
- Divya Bajpai Tripathy
- Division of Chemistry, School of Basic Sciences, Galgotias University, Greater Noida 201312, India;
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25
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Huang M, Ali W, Yang L, Huang J, Yao C, Xie Y, Sun R, Zhu C, Tan Y, Liu X, Li S, Li Z, Pan A. Multifunctional Optoelectronic Synapses Based on Arrayed MoS 2 Monolayers Emulating Human Association Memory. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300120. [PMID: 37058134 DOI: 10.1002/advs.202300120] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Indexed: 06/04/2023]
Abstract
Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the successful growth of MoS2 monolayer arrays assisted by gold nanorods guided precursor nucleation is demonstrated. Optical, spectral, and morphology characterizations of MoS2 prove that arrayed flakes are homogeneous monolayers, and they are further fabricated as optoelectronic devices showing featured photocurrent loops and stable optical responses. Typical synaptic behaviors of photo-induced short-term potentiation, long-term potentiation, and paired pulse facilitation are recorded under different light stimulations of 450, 532, and 633 nm lasers at various excitation powers. A visual sensing system consisting of 5 × 6 pixels is constructed to simulate the light-sensing image mapped by forgetting curves in real time. Moreover, the system presents the ability of utilizing associated images to restore vague and incomplete memories, which successfully mimics human intelligent behaviors of association memory and logical reasoning. The work emulates the brain-like artificial intelligence using arrayed 2D semiconductors, which paves an avenue to achieve smart retina and complex brain-like system.
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Affiliation(s)
- Ming Huang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Wajid Ali
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Liuli Yang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Jianhua Huang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Chengdong Yao
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Yunfei Xie
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Ronghuan Sun
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Chenguang Zhu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Yike Tan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Xiao Liu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Shengman Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Ziwei Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Anlian Pan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
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26
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Zhang Y, Huang Z, Jiang J. Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2186689. [PMID: 37007672 PMCID: PMC10054230 DOI: 10.1080/14686996.2023.2186689] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.
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Affiliation(s)
- Yi Zhang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Zhuohui Huang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
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27
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Rodrigues AF, Rebelo C, Reis T, Simões S, Bernardino L, Peça J, Ferreira L. Engineering optical tools for remotely controlled brain stimulation and regeneration. Biomater Sci 2023; 11:3034-3050. [PMID: 36947145 DOI: 10.1039/d2bm02059a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2023]
Abstract
Neurological disorders are one of the world's leading medical and societal challenges due to the lack of efficacy of the first line treatment. Although pharmacological and non-pharmacological interventions have been employed with the aim of regulating neuronal activity and survival, they have failed to avoid symptom relapse and disease progression in the vast majority of patients. In the last 5 years, advanced drug delivery systems delivering bioactive molecules and neuromodulation strategies have been developed to promote tissue regeneration and remodel neuronal circuitry. However, both approaches still have limited spatial and temporal precision over the desired target regions. While external stimuli such as electromagnetic fields and ultrasound have been employed in the clinic for non-invasive neuromodulation, they do not have the capability of offering single-cell spatial resolution as light stimulation. Herein, we review the latest progress in this area of study and discuss the prospects of using light-responsive nanomaterials to achieve on-demand delivery of drugs and neuromodulation, with the aim of achieving brain stimulation and regeneration.
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Affiliation(s)
- Artur Filipe Rodrigues
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
| | - Catarina Rebelo
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
- Faculty of Medicine, Pólo das Ciências da Saúde, Unidade Central, University of Coimbra, 3000-354 Coimbra, Portugal.
| | - Tiago Reis
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
- Faculty of Medicine, Pólo das Ciências da Saúde, Unidade Central, University of Coimbra, 3000-354 Coimbra, Portugal.
| | - Susana Simões
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
- Faculty of Medicine, Pólo das Ciências da Saúde, Unidade Central, University of Coimbra, 3000-354 Coimbra, Portugal.
| | - Liliana Bernardino
- Health Sciences Research Centre, Faculty of Health Sciences, University of Beira Interior, 6201-506 Covilhã, Portugal
| | - João Peça
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
- Faculty of Medicine, Pólo das Ciências da Saúde, Unidade Central, University of Coimbra, 3000-354 Coimbra, Portugal.
| | - Lino Ferreira
- Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-517 Coimbra, Portugal.
- Institute of Interdisciplinary Research, University of Coimbra, 3000-354 Coimbra, Portugal
- Faculty of Medicine, Pólo das Ciências da Saúde, Unidade Central, University of Coimbra, 3000-354 Coimbra, Portugal.
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28
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Xue Z, Xu Y, Jin C, Liang Y, Cai Z, Sun J. Halide perovskite photoelectric artificial synapses: materials, devices, and applications. NANOSCALE 2023; 15:4653-4668. [PMID: 36805124 DOI: 10.1039/d2nr06403k] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In recent years, there has been a research boom on halide perovskites (HPs) whose outstanding performance in photovoltaic and optoelectronic fields is obvious to all. In particular, HP materials find application in the development of artificial synapses. HP-based synapses have great potential for artificial neuromorphic systems, which is due to their outstanding optoelectronic properties, femtojoule-level energy consumption, and simple fabrication process. In this review, we present the physical properties of HPs and describe two types of synaptic devices including two-terminal (2T) memristors and three-terminal (3T) transistors. The HP layer in 2T memristors can realize the change in the device conductance through physical mechanisms dominated by ion migration. On the other hand, HPs in 3T transistors can be used as efficient light-absorbing layers and rely on some special device structures to provide reliable current changes. In the final section of the article, we discuss some of the existing applications of HP-based synapses and bottlenecks to be solved.
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Affiliation(s)
- Zhengyang Xue
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
| | - Yunchao Xu
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
| | - Chenxing Jin
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
| | - Yihuan Liang
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
| | - Zihao Cai
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
| | - Jia Sun
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South, University, Changsha, Hunan 410083, P. R. China.
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China
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29
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Wang Y, Wang K, Hu X, Wang Y, Gao W, Zhang Y, Liu Z, Zheng Y, Xu K, Yang D, Pi X. Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility. ACS NANO 2023; 17:3696-3704. [PMID: 36745006 DOI: 10.1021/acsnano.2c10816] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Given the synergy of optogenetics and bioimaging in neuroscience, it is possible for light to simultaneously modulate and visualize synaptic events of optoelectronic synaptic devices, which are building blocks of a neuromorphic computing system with optoelectronic integration. Here we demonstrate the realization of the simultaneous modulation and visualization of synaptic events by using optically stimulated synaptic devices based on the heterostructure of fluorescent silicon quantum dots (Si QDs) and monolayer molybdenum disulfide (MoS2). The charge-transfer-enabled photogating effect of the Si QDs/MoS2 heterostructure leads to the nonvolatility of the synaptic devices, which exhibit important synaptic functionalities and synchronous fluorescence upon optical stimulation. An array of the Si QDs/MoS2 optoelectronic synaptic devices is well-employed to mimic robust neural population coding. Defective devices in this array may be pinpointed by the absence of their fluorescence. This work has an important implication for the development of synaptic devices facilitating the system-level diagnosis and device-level positioning of a neuromorphic computing system.
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Affiliation(s)
- Yue Wang
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang311215, China
| | - Kun Wang
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
| | - Xiangyu Hu
- Zhejiang Province Key Laboratory of Quantum Technology and Device & Department of Physics, Zhejiang University, Hangzhou, Zhejiang310027, China
| | - Ya'kun Wang
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, Jiangsu215123, China
| | - Wandong Gao
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang311215, China
| | - Yiqiang Zhang
- School of Materials Science and Engineering & College of Chemistry, Zhengzhou University, Zhengzhou, Henan450001, China
| | - Zhenghui Liu
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, Jiangsu215123, China
| | - Yi Zheng
- Zhejiang Province Key Laboratory of Quantum Technology and Device & Department of Physics, Zhejiang University, Hangzhou, Zhejiang310027, China
| | - Ke Xu
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, Jiangsu215123, China
| | - Deren Yang
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang311215, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang311215, China
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30
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Dodda A, Jayachandran D, Subbulakshmi Radhakrishnan S, Pannone A, Zhang Y, Trainor N, Redwing JM, Das S. Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting. ACS NANO 2022; 16:20010-20020. [PMID: 36305614 DOI: 10.1021/acsnano.2c02906] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.
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Affiliation(s)
- Akhil Dodda
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | | | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yikai Zhang
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering and Computer Science, Penn State University, University Park, Pennsylvania 16802, United States
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31
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Chizhov A, Rumyantseva M, Khmelevsky N, Grunin A. Sensitization of ZnO Photoconductivity in the Visible Range by Colloidal Cesium Lead Halide Nanocrystals. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:4316. [PMID: 36500938 PMCID: PMC9740271 DOI: 10.3390/nano12234316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
In this work, colloidal perovskite nanocrystals (PNCs) are used to sensitize the photoconductivity of nanocrystalline ZnO films in the visible range. Nanocrystalline ZnO with a crystallite size of 12-16 nm was synthesized by precipitation of a zinc basic carbonate from an aqueous solution, followed by annealing at 300 °C. Perovskite oleic acid- and oleylamine-capped CsPbBr3, CsPb(Cl/Br)3 and CsPb(Br/I)3 PNCs with a size of 6-13 nm were synthesized by a hot injection method at 170 °C in 1-octadecene. Photoconductive nanocomposites were prepared by applying a hexane sol of PNCs to a thick (100 μm) polycrystalline conductive ZnO layer. The spectral dependence of the photoconductivity, the dependence of the photoconductivity on irradiation, and the relaxation of the photoconductivity of the obtained nanocomposites have been studied. Sensitization of ZnO by CsPbBr3 and CsPb(Cl/Br)3 PNCs leads to enhanced photoconductivity in the visible range, the maximum of which is observed at 460 and 500 nm, respectively; close to the absorption maximum of PNCs. Nanocomposites ZnO/CsPb(Br/I)3 turned out to be practically not photosensitive when irradiated with light in the visible range. The data obtained are discussed in terms of the position of the energy levels of ZnO and PNCs and the probable PNCs photodegradation. The structure, morphology, composition, and optical properties of the synthesized nanocrystals have also been studied by XRD, TEM, and XPS. The results can be applied to the creation of artificial neuromorphic systems in the visible optical range.
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Affiliation(s)
- Artem Chizhov
- Chemistry Department, Moscow State University, 119991 Moscow, Russia
| | | | - Nikolay Khmelevsky
- Material Properties Research Laboratory (LISM), Moscow State Technological University Stankin,127055 Moscow, Russia
| | - Andrey Grunin
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
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A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation. Nat Commun 2022; 13:7427. [PMID: 36460638 PMCID: PMC9718817 DOI: 10.1038/s41467-022-35092-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/17/2022] [Indexed: 12/04/2022] Open
Abstract
Brain-inspired electronics require artificial synapses that have ultra-low energy consumption, high operating speed, and stable flexibility. Here, we demonstrate a flexible artificial synapse that uses a rapidly crystallized perovskite layer at room temperature. The device achieves a series of synaptic functions, including logical operations, temporal and spatial rules, and associative learning. Passivation using phenethyl-ammonium iodide eliminated defects and charge traps to reduce the energy consumption to 13.5 aJ per synaptic event, which is the world record for two-terminal artificial synapses. At this ultralow energy consumption, the device achieves ultrafast response frequency of up to 4.17 MHz; which is orders of magnitude magnitudes higher than previous perovskite artificial synapses. A multi-stimulus accumulative artificial neuromuscular system was then fabricated using the perovskite synapse as a key processing unit to control electrochemical artificial muscles, and realized muscular-fatigue warning. This artificial synapse will have applications in future bio-inspired electronics and neurorobots.
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Yang YF, Lin YC, Ercan E, Chiang YC, Lin BH, Chen WC. Improving the Photoresponse of Transistor Memory Using Self-Assembled Nanostructured Block Copolymers as a Photoactive Electret. Macromolecules 2022. [DOI: 10.1021/acs.macromol.2c01634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yun-Fang Yang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Yan-Cheng Lin
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei 10617, Taiwan
| | - Ender Ercan
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei 10617, Taiwan
| | - Yun-Chi Chiang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Bi-Hsuan Lin
- National Synchrotron Radiation Research Center, Hsinchu 30076, Taiwan
| | - Wen-Chang Chen
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei 10617, Taiwan
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Wang C, Xu X, Pi X, Butala MD, Huang W, Yin L, Peng W, Ali M, Bodepudi SC, Qiao X, Xu Y, Sun W, Yang D. Neuromorphic device based on silicon nanosheets. Nat Commun 2022; 13:5216. [PMID: 36064545 PMCID: PMC9445003 DOI: 10.1038/s41467-022-32884-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.
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Affiliation(s)
- Chenhao Wang
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China
| | - Xinyi Xu
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China
- State Key Laboratory of Silicon Materials & School of Micro-Nanoelectronics, Zhejiang University, 310027, Hangzhou, PR China
- College of Information Science and Electronics Engineering, Zhejiang University, 310027, Hangzhou, PR China
- ZJU-UIUC Institute (ZJUI), Zhejiang University, 314400, Jiaxing, PR China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China
| | - Mark D Butala
- ZJU-UIUC Institute (ZJUI), Zhejiang University, 314400, Jiaxing, PR China
| | - Wen Huang
- New Energy Technology Engineering Laboratory of Jiangsu Provence & School of Science, Nanjing University of Posts and Telecommunications, 210023, Nanjing, PR China
| | - Lei Yin
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China
| | - Wenbing Peng
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China
| | - Munir Ali
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China
- State Key Laboratory of Silicon Materials & School of Micro-Nanoelectronics, Zhejiang University, 310027, Hangzhou, PR China
| | - Srikrishna Chanakya Bodepudi
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China
- State Key Laboratory of Silicon Materials & School of Micro-Nanoelectronics, Zhejiang University, 310027, Hangzhou, PR China
| | - Xvsheng Qiao
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China
| | - Yang Xu
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China.
- State Key Laboratory of Silicon Materials & School of Micro-Nanoelectronics, Zhejiang University, 310027, Hangzhou, PR China.
- College of Information Science and Electronics Engineering, Zhejiang University, 310027, Hangzhou, PR China.
- ZJU-UIUC Institute (ZJUI), Zhejiang University, 314400, Jiaxing, PR China.
| | - Wei Sun
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China.
| | - Deren Yang
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, 310027, Hangzhou, PR China.
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China.
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Shi J, Jie J, Deng W, Luo G, Fang X, Xiao Y, Zhang Y, Zhang X, Zhang X. A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200380. [PMID: 35243701 DOI: 10.1002/adma.202200380] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.
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Affiliation(s)
- Jialin Shi
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jiansheng Jie
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
- Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR, 999078, P. R. China
| | - Wei Deng
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Gan Luo
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaochen Fang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yanling Xiao
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yujian Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiujuan Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaohong Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
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Liu Q, Gao S, Xu L, Yue W, Zhang C, Kan H, Li Y, Shen G. Nanostructured perovskites for nonvolatile memory devices. Chem Soc Rev 2022; 51:3341-3379. [PMID: 35293907 DOI: 10.1039/d1cs00886b] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.
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Affiliation(s)
- Qi Liu
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Song Gao
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Lei Xu
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Wenjing Yue
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Chunwei Zhang
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Hao Kan
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Yang Li
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China. .,State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors & Chinese Academy of Sciences and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China.
| | - Guozhen Shen
- State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors & Chinese Academy of Sciences and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China.
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37
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Zhang J, Liu D, Ou Q, Lu Y, Huang J. Covalent Coupling of Porphyrins with Monolayer Graphene for Low-Voltage Synaptic Transistors. ACS APPLIED MATERIALS & INTERFACES 2022; 14:11699-11707. [PMID: 35213150 DOI: 10.1021/acsami.1c22073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Synaptic devices emulating biological synapses are a key building component of artificial neural networks. Porphyrins and graphene, as two kinds of emerging electronic materials, have attracted extensive attention in the research of photoelectric devices due to their excellent structural and functional properties. Herein, we present a photonic synaptic transistor based on porphyrin-graphene covalent hybrids utilizing 5,10,15,20-tetrakis (4-aminophenyl)-21H,23H-porphine and monolayer graphene linked through the diazo addition reaction. The photonic synaptic device successfully simulates several essential biological functions, and the synaptic plasticity can be regulated by adjusting the parameters of light spikes and gate voltages of the device. Moreover, learning and memory behaviors under different wavelengths are studied to imitate the learning efficiency of humans in diverse emotional states. It is worth noting that all the synaptic functions can be realized at a low operating voltage of -10 mV, which is much lower than that required by most reported photonic synaptic devices. These results indicate that covalent coupling products of porphyrins with graphene have broad prospects in the construction of synaptic transistors and may arouse new research advances in neuromorphic devices with ultralow operating voltage and low energy consumption.
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Affiliation(s)
- Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, P. R. China
| | - Dapeng Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, P. R. China
| | - Qingqing Ou
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, P. R. China
| | - Yang Lu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, P. R. China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, School of Materials Science and Engineering, Tongji University, Shanghai 200434, P. R. China
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38
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Xu Z, Ni Y, Han H, Wei H, Liu L, Zhang S, Huang H, Xu W. A hybrid ambipolar synaptic transistor emulating multiplexed neurotransmission for motivation control and experience-dependent learning. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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39
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Li J, Xin M, Ma Z, Shi Y, Pan L. Nanomaterials and their applications on bio-inspired wearable electronics. NANOTECHNOLOGY 2021; 32:472002. [PMID: 33592596 DOI: 10.1088/1361-6528/abe6c7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Wearable electronics featuring conformal attachment, sensitive perception and intellectual signal processing have made significant progress in recent years. However, when compared with living organisms, artificial sensory devices showed undeniable bulky shape, poor adaptability, and large energy consumption. To make up for the deficiencies, biological examples provide inspirations of novel designs and practical applications. In the field of biomimetics, nanomaterials from nanoparticles to layered two-dimensional materials are actively involved due to their outstanding physicochemical properties and nanoscale configurability. This review focuses on nanomaterials related to wearable electronics through bioinspired approaches on three different levels, interfacial packaging, sensory structure, and signal processing, which comprehensively guided recent progress of wearable devices in leveraging both nanomaterial superiorities and biorealistic functionalities. In addition, opinions on potential development trend are proposed aiming at implementing bioinspired electronics in multifunctional portable sensors, health monitoring, and intelligent prosthetics.
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Affiliation(s)
- Jiean Li
- Collaborative Innovation Center of Advanced Microstructures, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
| | - Ming Xin
- Collaborative Innovation Center of Advanced Microstructures, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
| | - Zhong Ma
- Collaborative Innovation Center of Advanced Microstructures, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
| | - Yi Shi
- Collaborative Innovation Center of Advanced Microstructures, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
| | - Lijia Pan
- Collaborative Innovation Center of Advanced Microstructures, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
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40
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Su Y, Wang C, Hong Z, Sun W. Thermal Disproportionation for the Synthesis of Silicon Nanocrystals and Their Photoluminescent Properties. Front Chem 2021; 9:721454. [PMID: 34458238 PMCID: PMC8397416 DOI: 10.3389/fchem.2021.721454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Abstract
In the past decades, silicon nanocrystals have received vast attention and have been widely studied owing to not only their advantages including nontoxicity, high availability, and abundance but also their unique luminescent properties distinct from bulk silicon. Among the various synthetic methods of silicon nanocrystals, thermal disproportionation of silicon suboxides (often with H as another major composing element) bears the superiorities of unsophisticated equipment requirements, feasible processing conditions, and precise control of nanocrystals size and structure, which guarantee a bright industrial application prospect. In this paper, we summarize the recent progress of thermal disproportionation chemistry for the synthesis of silicon nanocrystals, with the focus on the effects of temperature, Si/O ratio, and the surface groups on the resulting silicon nanocrystals’ structure and their corresponding photoluminescent properties. Moreover, the paradigmatic application scenarios of the photoluminescent silicon nanocrystals synthesized via this method are showcased or envisioned.
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Affiliation(s)
- Yize Su
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
| | - Chenhao Wang
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
| | - Zijian Hong
- Lab of Dielectric Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
| | - Wei Sun
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
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41
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Yang B, Wang Y, Hua Z, Zhang J, Li L, Hao D, Guo P, Xiong L, Huang J. Low-power consumption light-stimulated synaptic transistors based on natural carotene and organic semiconductors. Chem Commun (Camb) 2021; 57:8300-8303. [PMID: 34318806 DOI: 10.1039/d1cc03060d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Developing synaptic devices with environment-friendly materials is a promising research direction. Here, light-stimulated synaptic transistors based on natural carotene and organic semiconductors were developed. Several important functions similar to biological synapses were realized and an ultra-low power consumption of 3.4 × 10-18 J was achieved.
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Affiliation(s)
- Ben Yang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, 201804, P. R. China.
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42
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Zhang J, Lu Y, Dai S, Wang R, Hao D, Zhang S, Xiong L, Huang J. Retina-Inspired Organic Heterojunction-Based Optoelectronic Synapses for Artificial Visual Systems. RESEARCH 2021; 2021:7131895. [PMID: 33709082 PMCID: PMC7926506 DOI: 10.34133/2021/7131895] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/17/2021] [Indexed: 12/13/2022]
Abstract
For the realization of retina-inspired neuromorphic visual systems which simulate basic functions of human visual systems, optoelectronic synapses capable of combining perceiving, processing, and memorizing in a single device have attracted immense interests. Here, optoelectronic synaptic transistors based on tris(2-phenylpyridine) iridium (Ir(ppy)3) and poly(3,3-didodecylquarterthiophene) (PQT-12) heterojunction structure are presented. The organic heterojunction serves as a basis for distinctive synaptic characteristics under different wavelengths of light. Furthermore, synaptic transistor arrays are fabricated to demonstrate their optical perception efficiency and color recognition capability under multiple illuminating conditions. The wavelength-tunability of synaptic behaviors further enables the mimicry of mood-modulated visual learning and memorizing processes of humans. More significantly, the computational dynamics of neurons of synaptic outputs including associated learning and optical logic functions can be successfully demonstrated on the presented devices. This work may locate the stage for future studies on optoelectronic synaptic devices toward the implementation of artificial visual systems.
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Affiliation(s)
- Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Yang Lu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Shilei Dai
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Ruizhi Wang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Dandan Hao
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Shiqi Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai 200434, China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China.,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai 200434, China
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Huang W, Xia X, Zhu C, Steichen P, Quan W, Mao W, Yang J, Chu L, Li X. Memristive Artificial Synapses for Neuromorphic Computing. NANO-MICRO LETTERS 2021; 13:85. [PMID: 34138298 PMCID: PMC8006524 DOI: 10.1007/s40820-021-00618-2] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 01/29/2021] [Indexed: 05/06/2023]
Abstract
Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
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Affiliation(s)
- Wen Huang
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
| | - Xuwen Xia
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China
| | - Chen Zhu
- College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China
| | - Parker Steichen
- Department of Materials Science and Engineering, University of Washington, Seattle, WA, 98195-2120, USA
| | - Weidong Quan
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China
| | - Weiwei Mao
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China
| | - Jianping Yang
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China
| | - Liang Chu
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
| | - Xing'ao Li
- New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
- Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials, Jiangsu National Synergistic Innovation Center for Advanced Materials, School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications (NUPT), 9 Wenyuan Road, Nanjing, 210023, People's Republic of China.
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Kumar M, Lim J, Kim S, Seo H. Environment-Adaptable Photonic-Electronic-Coupled Neuromorphic Angular Visual System. ACS NANO 2020; 14:14108-14117. [PMID: 32985189 DOI: 10.1021/acsnano.0c06874] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Environment-adaptable photonic-electronic-coupled devices can help overcome major challenges related to the extraction of highly specific angular information, such as human visual perception. However, a true implementation of such a device has rarely been investigated thus far. Herein, we provide an approach and demonstrate a proof-of-concept solid-state semiconductor-based highly transparent, optical-electrical-coupled, self-adaptive angular visual perception system that can fulfill the versatile criteria of the human vision system. Specifically, all of the primitive functions of visual perception, such as broad angular sensing, processing, and manifold memory storage, are demonstrated and comodulated using optical and electric pulses. This development represents an essential step forward in the fabrication of an environment-adaptable artificial angular perception framework with deep implications in the fields of optoelectronics, artificial eyes, and memory storage applications.
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Affiliation(s)
- Mohit Kumar
- Department of Materials Science and Engineering, Ajou University, Suwon 16499, Republic of Korea
| | - Jaeseong Lim
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Sangwan Kim
- Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea
| | - Hyungtak Seo
- Department of Materials Science and Engineering, Ajou University, Suwon 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
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Zhu Y, Huang W, He Y, Yin L, Zhang Y, Yang D, Pi X. Perovskite-Enhanced Silicon-Nanocrystal Optoelectronic Synaptic Devices for the Simulation of Biased and Correlated Random-Walk Learning. RESEARCH 2020; 2020:7538450. [PMID: 33015636 PMCID: PMC7510342 DOI: 10.34133/2020/7538450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 07/30/2020] [Indexed: 11/21/2022]
Abstract
Silicon- (Si-) based optoelectronic synaptic devices mimicking biological synaptic functionalities may be critical to the development of large-scale integrated optoelectronic artificial neural networks. As a type of important Si materials, Si nanocrystals (NCs) have been successfully employed to fabricate optoelectronic synaptic devices. In this work, organometal halide perovskite with excellent optical asborption is employed to improve the performance of optically stimulated Si-NC-based optoelectronic synaptic devices. The improvement is evidenced by the increased optical sensitivity and decreased electrical energy consumption of the devices. It is found that the current simulation of biological synaptic plasticity is essentially enabled by photogating, which is based on the heterojuction between Si NCs and organometal halide perovskite. By using the synaptic plasticity, we have simulated the well-known biased and correlated random-walk (BCRW) learning.
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Affiliation(s)
- Yiyue Zhu
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Wen Huang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yifei He
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Lei Yin
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yiqiang Zhang
- School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Deren Yang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China.,Institute of Advanced Semiconductors, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang 311215, China
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46
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Hao D, Zhang J, Dai S, Zhang J, Huang J. Perovskite/Organic Semiconductor-Based Photonic Synaptic Transistor for Artificial Visual System. ACS APPLIED MATERIALS & INTERFACES 2020; 12:39487-39495. [PMID: 32805934 DOI: 10.1021/acsami.0c10851] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Artificial visual system with information sensing, processing, and memory function is promoting the development of artificial intelligence techniques. Photonic synapse as an essential component can enhance the visual information processing efficiency owing to the high propagation speed, low latency, and large bandwidth. Herein, photonic synaptic transistors based on organic semiconductor poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno [3,2-b]thiophene)] (DPPDTT) and perovskite CsPbBr3 quantum dots are fabricated by a simple solution process. The device can simulate fundamental synaptic behaviors, including excitatory postsynaptic current, pair-pulse facilitation, the transition of short-term memory to long-term memory, and "learning experience" behavior. Combining the advantages of the high photosensitivity of perovskites and relatively high conductivity of DPPDTT, the device can exhibit excellent synaptic performances at a low voltage of -0.2 V. Even under an ultralow operation voltage of -0.0005 V, the device can still show obvious synaptic responses. Tunable synaptic integration behaviors including "AND" and "OR" light logic functions can be realized. An artificial visual system is successfully emulated by illuminating the synaptic arrays employing light of different densities. Therefore, low-voltage synaptic devices based on organic semiconductor and CsPbBr3 quantum dots with a simple fabrication technique present high potential to mimic human visual memory.
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Affiliation(s)
- Dandan Hao
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, P. R. China
| | - Shilei Dai
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, P. R. China
| | - Jianhua Zhang
- Key Laboratory of Advanced Display and System Application, Ministry of Education, Shanghai University, Shanghai 200072, P. R. China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, P. R. China
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