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Chen S, Zhou Z, Hou K, Wu X, He Q, Tang CG, Li T, Zhang X, Jie J, Gao Z, Mathews N, Leong WL. Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot. Nat Commun 2024; 15:7056. [PMID: 39147776 PMCID: PMC11327256 DOI: 10.1038/s41467-024-51403-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 08/05/2024] [Indexed: 08/17/2024] Open
Abstract
The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (-0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.
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Affiliation(s)
- Shuai Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, PR China
| | - Zhongliang Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Kunqi Hou
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Xihu Wu
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Qiang He
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Cindy G Tang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ting Li
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Xiujuan Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, PR 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, PR China
| | - Zhiyi Gao
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, PR China
| | - Nripan Mathews
- Energy Research Institute @ NTU, Nanyang Technological University, Singapore, Singapore.
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, Singapore.
| | - Wei Lin Leong
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
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2
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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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3
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Zhang T, Shao L, Jaafar A, Zeimpekis I, de Groot CH, Bartlett PN, Hector AL, Huang R. Tunable Neuromorphic Switching Dynamics via Porosity Control in Mesoporous Silica Diffusive Memristors. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16641-16652. [PMID: 38494599 PMCID: PMC10995907 DOI: 10.1021/acsami.3c19020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
In response to the growing need for efficient processing of temporal information, neuromorphic computing systems are placing increased emphasis on the switching dynamics of memristors. While the switching dynamics can be regulated by the properties of input signals, the ability of controlling it via electrolyte properties of a memristor is essential to further enrich the switching states and improve data processing capability. This study presents the synthesis of mesoporous silica (mSiO2) films using a sol-gel process, which enables the creation of films with controllable porosities. These films can serve as electrolyte layers in the diffusive memristors and lead to tunable neuromorphic switching dynamics. The mSiO2 memristors demonstrate short-term plasticity, which is essential for temporal signal processing. As porosity increases, discernible changes in operating currents, facilitation ratios, and relaxation times are observed. The underlying mechanism of such systematic control was investigated and attributed to the modulation of hydrogen-bonded networks within the porous structure of the silica layer, which significantly influences both anodic oxidation and ion migration processes during switching events. The result of this work presents mesoporous silica as a unique platform for precise control of neuromorphic switching dynamics in diffusive memristors.
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Affiliation(s)
- Tongjun Zhang
- School
of Electronics and Computer Science, University
of Southampton, Southampton SO17 1BJ, United
Kingdom
| | - Li Shao
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Ayoub Jaafar
- School
of Electronics and Computer Science, University
of Southampton, Southampton SO17 1BJ, United
Kingdom
| | - Ioannis Zeimpekis
- School
of Electronics and Computer Science, University
of Southampton, Southampton SO17 1BJ, United
Kingdom
| | - Cornelis H. de Groot
- School
of Electronics and Computer Science, University
of Southampton, Southampton SO17 1BJ, United
Kingdom
| | - Philip N. Bartlett
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Andrew L. Hector
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Ruomeng Huang
- School
of Electronics and Computer Science, University
of Southampton, Southampton SO17 1BJ, United
Kingdom
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4
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Lu C, Meng J, Yu J, Song J, Wang T, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Three-Dimensional Artificial Neural Network Based on an Eight-Layer Vertical Memristor with an Ultrahigh Rectify Ratio (>10 7) and an Ultrahigh Nonlinearity (>10 5) for Neuromorphic Computing. NANO LETTERS 2024; 24:2018-2024. [PMID: 38315050 DOI: 10.1021/acs.nanolett.3c04577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
In recent years, memristors have successfully demonstrated their significant potential in artificial neural networks (ANNs) and neuromorphic computing. Nonetheless, ANNs constructed by crossbar arrays suffer from cross-talk issues and low integration densities. Here, we propose an eight-layer three-dimensional (3D) vertical crossbar memristor with an ultrahigh rectify ratio (RR > 107) and an ultrahigh nonlinearity (>105) to overcome these limitations, which enables it to reach a >1 Tb array size without reading failure. Furthermore, the proposed 3D RRAM shows advanced endurance (>1010 cycles), retention (>104 s), and uniformity. In addition, several synaptic functions observed in the human brain were mimicked. On the basis of the advanced performance, we constructed a novel 3D ANN, whose learning efficiency and recognition accuracy were enhanced significantly compared with those of conventional single-layer ANNs. These findings hold promise for the development of highly efficient, precise, integrated, and stable VLSI neuromorphic computing systems.
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Affiliation(s)
- Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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5
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Lu C, Meng J, Song J, Wang T, Zhu H, Sun QQ, Zhang DW, Chen L. Self-Rectifying All-Optical Modulated Optoelectronic Multistates Memristor Crossbar Array for Neuromorphic Computing. NANO LETTERS 2024; 24:1667-1672. [PMID: 38241735 DOI: 10.1021/acs.nanolett.3c04358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
Researching optoelectronic memristors capable of integrating sensory and processing functions is essential for advancing the development of efficient neuromorphic vision. Here, we experimentally demonstrated an all-optical controlled and self-rectifying optoelectronic memristor (OEM) crossbar array with the function of multilevel storage under light stimuli. The NiO/TiO2 device exhibits an ultrahigh (>104) rectifying ratio (RR) thus overcoming the presence of sneak current. The reversible conductance modulation without electric signal involvement provides a novel way to realize ultrafast information processing. The proposed OEM array realized synaptic functions observed in the human brain, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), the transition from short-term memory (STM) to long-term memory (LTM), and learning experience behaviors successfully. The authors present a novel OEM crossbar that possesses complete light-modulation capabilities, potentially advancing the future development of efficient neuromorphic vision.
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Affiliation(s)
- Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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6
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Hasina D, Saini M, Kumar M, Mandal A, Basu N, Maiti P, Srivastava SK, Som T. Site-Specific Emulation of Neuronal Synaptic Behavior in Au Nanoparticle-Decorated Self-Organized TiO x Surface. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2305605. [PMID: 37803918 DOI: 10.1002/smll.202305605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/18/2023] [Indexed: 10/08/2023]
Abstract
Neuromorphic computing is a potential approach for imitating massive parallel processing capabilities of a bio-synapse. To date, memristors have emerged as the most appropriate device for designing artificial synapses for this purpose due to their excellent analog switching capacities with high endurance and retention. However, to build an operational neuromorphic platform capable of processing high-density information, memristive synapses with nanoscale footprint are important, albeit with device size scaled down, retaining analog plasticity and low power requirement often become a challenge. This paper demonstrates site-selective self-assembly of Au nanoparticles on a patterned TiOx layer formed as a result of ion-induced self-organization, resulting in site-specific resistive switching and emulation of bio-synaptic behavior (e.g., potentiation, depression, spike rate-dependent and spike timing-dependent plasticity, paired pulse facilitation, and post tetanic potentiation) at nanoscale. The use of local probe-based methods enables nanoscale probing on the anisotropic films. With the help of various microscopic and spectroscopic analytical tools, the observed results are attributed to defect migration and self-assembly of implanted Au atoms on self-organized TiOx surfaces. By leveraging the site-selective evolution of gold-nanostructures, the functionalized TiOx surface holds significant potential in a multitude of fields for developing cutting-edge neuromorphic computing platforms and Au-based biosensors with high-density integration.
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Affiliation(s)
- Dilruba Hasina
- SUNAG Laboratory, Institute of Physics, Sachivalaya Marg, Bhubaneswar, 751005, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, 400085, India
| | - Mahesh Saini
- SUNAG Laboratory, Institute of Physics, Sachivalaya Marg, Bhubaneswar, 751005, India
| | - Mohit Kumar
- Department of Materials Science and Engineering, Ajou University, Suwon, 16499, Republic of Korea
| | - Aparajita Mandal
- SUNAG Laboratory, Institute of Physics, Sachivalaya Marg, Bhubaneswar, 751005, India
| | - Nilanjan Basu
- School of Physics, University of Hyderabad, Hyderabad, 500046, India
| | - Paramita Maiti
- TEM Laboratory, Institute of Physics, 751005, Sachivalaya Marg, Bhubaneswar, India
| | | | - Tapobrata Som
- SUNAG Laboratory, Institute of Physics, Sachivalaya Marg, Bhubaneswar, 751005, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, 400085, India
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7
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Sun B, Chen Y, Zhou G, Cao Z, Yang C, Du J, Chen X, Shao J. Memristor-Based Artificial Chips. ACS NANO 2024; 18:14-27. [PMID: 38153841 DOI: 10.1021/acsnano.3c07384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.
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Affiliation(s)
- Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, People's Republic of China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Junmei Du
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Xiaoliang Chen
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Jinyou Shao
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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8
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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: 5] [Impact Index Per Article: 5.0] [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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9
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Li C, Zhang X, Chen P, Zhou K, Yu J, Wu G, Xiang D, Jiang H, Wang M, Liu Q. Short-term synaptic plasticity in emerging devices for neuromorphic computing. iScience 2023; 26:106315. [PMID: 36950108 PMCID: PMC10025973 DOI: 10.1016/j.isci.2023.106315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.
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Affiliation(s)
- Chao Li
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Pei Chen
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Keji Zhou
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Jie Yu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guangjian Wu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Du Xiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Ming Wang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Qi Liu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
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10
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Xia Q, Qin Y, Zheng A, Qiu P. A low-power and flexible bioinspired artificial sensory neuron capable of tactile perceptual and associative learning. J Mater Chem B 2023; 11:1469-1477. [PMID: 36655946 DOI: 10.1039/d2tb02408j] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Biomimetic haptic neuron systems have received a lot of attention from the booming artificial intelligence industry for their wide applications in personal health monitoring, electronic skin, and human-machine interfaces. In this work, inspired by the human tactile afferent nerve, we developed a flexible and low energy consumption artificial tactile neuron, which was constructed by combining a dual network (DN) hydrogel-based sensor and a low power memristor. The tactile sensor (ITO/PAM:CS-Fe3+ hydrogel/ITO) serves as E-skin, with mechanical properties including pressure and stretching. The memristor (Ti:ITO/BiFeO3/ITO) serving as an artificial synapse has low power (∼3.96 × 10-7 W), remarkable uniformity, a large memory window of 500 and excellent plasticity. Remarkably, the pattern recognition simulation based on a neuromorphic network is conducted with a high recognition accuracy of ∼89.81%. In the constructed system, the artificial synapse could be activated by the electrical information from the E-skin induced by an external pressure, to generate excitatory postsynaptic currents. The system shows functions of perception and memory functions, and it also enables tactile associative learning. The present work is important for the development of empowering robots and prostheses with the capability of perceptual learning, and it provides a paradigm for next-generation artificial sensory systems with low-power, wearable and low-cost features.
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Affiliation(s)
- Qing Xia
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Yuxiang Qin
- School of Microelectronics, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin University, Tianjin 300072, China. .,Key Laboratory for Advanced Ceramics and Machining Technology, Ministry of Education, School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Anbo Zheng
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Peilun Qiu
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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11
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Song YG, Kim JE, Kwon JU, Chun SY, Soh K, Nahm S, Kang CY, Yoon JH. Highly Reliable Threshold Switching Characteristics of Surface-Modulated Diffusive Memristors Immune to Atmospheric Changes. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5495-5503. [PMID: 36691225 DOI: 10.1021/acsami.2c21019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Active cation-based diffusive memristors featuring essentially volatile threshold switching have been proposed for novel applications, such as a selector in a one-selector-and-one-resistor structure and signal generators in neuromorphic computing. However, the high variability of the switching behavior, which results from the high electroforming voltage, external environmental conditions, and transition to the non-volatile switching mode in a high-current range, is considered a major impediment to such applications. Herein, for the first time, we developed a highly reliable threshold switching device immune to atmospheric changes based on an ultraviolet-ozone (UVO)-treated diffusive memristor consisting of Ag and SiO2 nanorods (NRs). UVO treatment forms a stable water reservoir on the surface of SiO2 NRs, facilitating the redox reaction and ion migration of Ag. Consequently, diffusive memristors possess reliable switching characteristics, including electroforming-free, repeatable, and consistent switching with resistance to changes in ambient conditions and compliance levels during operation. We demonstrated that our approach is suitable for various metal oxides and can be used in numerous applications.
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Affiliation(s)
- Young Geun Song
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Keunho Soh
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Sahn Nahm
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
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12
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Zhao J, Zheng S, Zhou L, Mi W, Ding Y, Wang M. An artificial optoelectronic synapse based on MoO xfilm. NANOTECHNOLOGY 2023; 34:145201. [PMID: 36630707 DOI: 10.1088/1361-6528/acb217] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Artificial optoelectronic synapses have the advantages of large bandwidth, low power consumption and low crosstalk, and are considered to be the basic building blocks of neuromorphic computing. In this paper, a two-terminal optoelectronic synaptic device with ITO-MoOx-Pt structure is prepared by magnetron sputtering. The performance of resistive switching (RS) and the photo plastic properties of the device are analyzed and demonstrated. Electrical characterization tests show that the device has a resistive HRS/LRS ratio of about 90, stable endurance, and retention characteristics of more than 104s (85 °C). The physical mechanism of the device is elucidated by a conducting filament composed of oxygen vacancies. Furthermore, the function of various synaptic neural morphologies is successfully mimicked using UV light as the stimulation source. Including short-term/long-term memory, paired-pulse facilitation, the transition from short-term to long-term memory, and 'learning-experience' behavior. Integrated optical sensing and electronic data storage devices have great potential for future artificial intelligence, which will facilitate the rapid development of retina-like visual sensors and low-power neuromorphic systems.
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Affiliation(s)
- Jinshi Zhao
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xi qing District, Tianjin, 300384, People's Republic of China
| | - ShuTong Zheng
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xi qing District, Tianjin, 300384, People's Republic of China
| | - Liwei Zhou
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xi qing District, Tianjin, 300384, People's Republic of China
| | - Wei Mi
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xi qing District, Tianjin, 300384, People's Republic of China
| | - Yue Ding
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xi qing District, Tianjin, 300384, People's Republic of China
| | - Meng Wang
- Innetech Electronics CO. Ltd, Building B, No. 4, Tianzhi Industrial Park, No. 12, Hongyuan Road, Xiqing Economic Development Zone, Tianjin, People's Republic of China
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13
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Jaafar AH, Shao L, Dai P, Zhang T, Han Y, Beanland R, Kemp NT, Bartlett PN, Hector AL, Huang R. 3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing. NANOSCALE 2022; 14:17170-17181. [PMID: 36380717 DOI: 10.1039/d2nr05012a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Memristors are emerging as promising candidates for practical application in reservoir computing systems that are capable of temporal information processing. Here, we experimentally implement a physical reservoir computing system using resistive memristors based on three-dimensional (3D)-structured mesoporous silica (mSiO2) thin films fabricated by a low cost, fast and vacuum-free sol-gel technique. The in situ learning capability and a classification accuracy of 100% on a standard machine learning dataset are experimentally demonstrated. The volatile (temporal) resistive switching in diffusive memristors arises from the formation and subsequent spontaneous rupture of conductive filaments via diffusion of Ag species within the 3D-structured nanopores of the mSiO2 thin film. Besides volatile switching, the devices also exhibit a bipolar non-volatile resistive switching behavior when the devices are operated at a higher compliance current level. The implementation of mSiO2 thin films opens the route to fabricate a simple and low cost dynamic memristor with a temporal information process functionality, which is essential for neuromorphic computing applications.
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Affiliation(s)
- Ayoub H Jaafar
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Li Shao
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Peng Dai
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Tongjun Zhang
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Yisong Han
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | - Richard Beanland
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | - Neil T Kemp
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Philip N Bartlett
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Andrew L Hector
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Ruomeng Huang
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
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14
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Li J, Fu W, Lei Y, Li L, Zhu W, Zhang J. Oxygen-Vacancy-Induced Synaptic Plasticity in an Electrospun InGdO Nanofiber Transistor for a Gas Sensory System with a Learning Function. ACS APPLIED MATERIALS & INTERFACES 2022; 14:8587-8597. [PMID: 35104096 DOI: 10.1021/acsami.1c23390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The perceptual learning function of a simulating human body is very important for constructing a neural computing system and a brainlike computer in the future. The sense of smell is an important part of the human sensory nervous system. However, current gas sensors simply convert gas concentrations into electrical signals and do not have the same learning and memory function as synapses. To solve this problem, we propose a new sensing idea to induce and activate the synaptic properties of transistors by adjusting the oxygen vacancy in the active layer. This sensor combines gas detection with synaptic memory and learning and overcomes the disadvantage of the separation of synaptic transistors and sensors, thus greatly reducing the cost of production. This work combines the detection of N,N-dimethylformamide (DMF) gas with the synaptic mechanism of human olfactory nerves. We successfully fabricated an InGdO nanofiber field-effect transistor by electrostatic spinning and simulated the response of human olfactory synapses to target gas by regulating the oxygen vacancy of the InGdO nanofiber. The synaptic transistor response under different concentrations of unmodulated pulses is tested, and the pavlovian conditioned reflex experiment is simulated successfully. This work provides a new idea of a gas sensor device, which is very important for the development of high-performance gas sensors and bionic electronic devices in the future.
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Affiliation(s)
- Jun Li
- School of Material Science and Engineering, Shanghai University, Jiading, Shanghai 201800, People's Republic of China
- Key Laboratory of Advanced Display and System Applications, Ministry of Education, Shanghai University, Shanghai 200072, People's Republic of China
| | - Wenhui Fu
- School of Material Science and Engineering, Shanghai University, Jiading, Shanghai 201800, People's Republic of China
| | - Yuxing Lei
- School of Material Science and Engineering, Shanghai University, Jiading, Shanghai 201800, People's Republic of China
| | - Linkang Li
- School of Material Science and Engineering, Shanghai University, Jiading, Shanghai 201800, People's Republic of China
| | - Wenqing Zhu
- School of Material Science and Engineering, Shanghai University, Jiading, Shanghai 201800, People's Republic of China
| | - Jianhua Zhang
- Key Laboratory of Advanced Display and System Applications, Ministry of Education, Shanghai University, Shanghai 200072, People's Republic of China
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15
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Meng Y, Zhu J. Low energy consumption fiber-type memristor array with integrated sensing-memory. NANOSCALE ADVANCES 2022; 4:1098-1104. [PMID: 36131775 PMCID: PMC9417447 DOI: 10.1039/d1na00703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/04/2022] [Indexed: 06/15/2023]
Abstract
The increasing growth of electronic information science and technology has triggered the renaissance of the artificial sensory nervous system (SNS), which can emulate the response of organisms towards external stimuli with high efficiency. In traditional SNS, the sensor units and the memory units are separated, and therefore difficult to miniaturize and integrate. Here, we have incorporated the sensor unit and the memory unit into one system, taking advantage of the unique properties of the ion-gel system. Meanwhile, the weaving-type memory array presents paramount advantages of integration and miniaturization and conformal lamination to curved surfaces. It is worth noting that the electrical double layer (EDL) within the ion gel endow the device with a low operation voltage (<1 V) to achieve low energy consumption. Finally, according to the relationship of pressure stimuli and electrical behavior, the integrated responsiveness-storage external stimuli ability is achieved. Our work offers a new platform for designing cutting edge SNS.
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Affiliation(s)
- Yanfang Meng
- Key Laboratory of Advanced Optical Communication Systems and Network, School of Electronic Engineering and Computer Science Department, Peking University Beijing 100091 China
- Department of Engineering Mechanics, Tsinghua University Beijing 100084 China
| | - Jiaxue Zhu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences Beitucheng West Road Beijing 100029 China
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16
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Tian G, Liu Y, Yu M, Liang C, Yang D, Huang J, Zhao Q, Zhang W, Chen J, Wang Y, Xu P, Liu Z, Qi D. Electrostatic Interaction-Based High Tissue Adhesive, Stretchable Microelectrode Arrays for the Electrophysiological Interface. ACS APPLIED MATERIALS & INTERFACES 2022; 14:4852-4861. [PMID: 35051334 DOI: 10.1021/acsami.1c18983] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The drift or fall of stretchable neural microelectrodes from the surface of wet and dynamic tissues severely hampers the adoption of microelectrodes for electrophysiological signal monitoring. Endowing the stretchable electrodes with adhesive ability is an effective way to overcome these problems. Current adhesives form tough adhesion to tissues by covalent interaction, which decreases the biocompatibility of the adhesives. Here, we fabricate a strong electrostatic adhesive (noncovalent interaction), highly conformal, stretchable microelectrode arrays (MEAs) for the electrophysiological interface. This MEA was composed of polypyrrole (PPy) as the electrode material and hydrogel as the stretchable substrate [the cross-linked and copolymerized hydrogel of 2-acrylamido-2-methylpropane sulfonic acid (AMPS), gelatin, chitosan, 2-methoxyethyl acrylate, and acrylic acid is named PAGMA]. Strong and stable electrostatic adhesion (85 kPa) and high stretchability (100%) allow for the integration of PPy MEAs based on the PAGMA hydrogel substrate (PPy-PAGMA MEAs) on diverse wet dynamic tissues. Additionally, by adjusting the concentration of AMPS in PAGMA, the hydrogel (PAGMA-1) can produce tough adhesion to many inorganic and elastomer materials. Finally, the PPy-PAGMA MEAs were toughly and conformally adhered on the rat's subcutaneous muscle and beating heart, and the rat's electrophysiological signals were successfully recorded. The development of these adhesive MEAs offers a promising strategy to establish stable and compliant electrode-tissue interfaces.
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Affiliation(s)
- Gongwei Tian
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Yan Liu
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Mei Yu
- Biomedical Microdevices Research Laboratory, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, P. R. China
| | - Cuiyuan Liang
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Dan Yang
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Jianping Huang
- Biomedical Microdevices Research Laboratory, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, P. R. China
| | - Qinyi Zhao
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Wei Zhang
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Jianhui Chen
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Yu Wang
- School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Ping Xu
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Zhiyuan Liu
- Biomedical Microdevices Research Laboratory, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, P. R. China
| | - Dianpeng Qi
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
- State Key Laboratory of Urban Water Resource and Environments, Harbin Institute of Technology, Harbin 150001, P. R. China
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17
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Abstract
Bio-memristors constitute candidates for the next generation of non-volatile storage and bionic synapses due to their biocompatibility, environmental benignity, sustainability, flexibility, degradability, and impressive memristive performance. Silk fibroin (SF), a natural and abundant biomaterial with excellent mechanical, optical, electrical, and structure-adjustable properties as well as being easy to process, has been utilized and shown to have potential in the construction of bio-memristors. Here, we first summarize the fundamental mechanisms of bio-memristors based on SF. Then, the latest achievements and developments of pristine and composited SF-based memristors are highlighted, followed by the integration of memristive devices. Finally, the challenges and insights associated with SF-based bio-memristors are presented. Advances in SF-based bio-memristors will open new avenues in the design and integration of high-performance bio-integrated systems and facilitate their application in logic operations, complex circuits, and neural networks.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China.
| | - Suna Fan
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China.
| | - Yaopeng Zhang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China.
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18
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Zhang S, Yang L, Jiang C, Sun L, Guo K, Han H, Xu W. Digitally aligned ZnO nanowire array based synaptic transistors with intrinsically controlled plasticity for short-term computation and long-term memory. NANOSCALE 2021; 13:19190-19199. [PMID: 34781328 DOI: 10.1039/d1nr04156h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Digitally aligned long continuous ZnO NWs with distinct widths and microstructures are prepared and used for tuning the plasticity of synaptic transistors (STs) for the first time. Intrinsically controlled synaptic plasticity, i.e. short-term plasticity (STP) and long-term plasticity (LTP), was achieved using the same source material and post-fabrication condition for the first time, which is essential for simple and low-cost fabrication. Moreover, these versatile properties of ZnO STs enable the integration of STP and LTP as realized by multiplexed neurotransmission of different neurotransmitters: dopamine and acetylcholine, which promote learning and memory in organisms, so the device may utilize these processes in neuroelectronic devices. Devices with well-controlled synaptic plasticity can simulate the "learning-forgetting-erase" and "instant display" processes. ZnO NWs may enable the development of neuromorphic computers that can use the same material to achieve both short-term computation and long-term memory.
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Affiliation(s)
- Shuo Zhang
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Lu Yang
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Chengpeng Jiang
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Lin Sun
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Kexin Guo
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Hong Han
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
| | - Wentao Xu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, National Institute of Advanced Materials, Nankai University, Tianjin 300350, China.
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19
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Liu Y, Wang J, Teng W, Hung CT, Zhai Y, Shen D, Li W. Ultrahigh Adsorption Capacity and Kinetics of Vertically Oriented Mesoporous Coatings for Removal of Organic Pollutants. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2101363. [PMID: 34216424 DOI: 10.1002/smll.202101363] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/22/2021] [Indexed: 06/13/2023]
Abstract
Highly efficient removal of organic pollutants currently is a main worldwide concern in water treatment, and highly challenging. Here, vertically oriented mesoporous coatings (MCs) with tunable surface properties and pore sizes have been developed via the single-micelle directing assembly strategy, which show good adsorption performances toward a wide range of organic pollutants. The micelle size and structure can be precisely regulated by oil molecules based on their n-octanol/water partition coefficients (Log P) in the oil-water diphase assembly system, which are critical to the pore size and pore surface property of the MCs. The affinity and steric effects of the MCs can be on-demand adjusted, as a result, the MCs show a ultrahigh adsorption capacity (263 mg g-1 ), surface occupancy ratio (≈41.92%), and adsorption rate (≈10.85 mg g-1 min-1 ) for microcystin-LR, which is among the best performances up to date. The MCs also show an excellent universality to remove organic pollutants with different properties. Moreover, overcoming the challenges proposed by particulate absorbents, the MCs are stable and can be easily regenerated and reused without secondary contamination. This work paves a new route to the synthesis of high-quality MCs for water purification.
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Affiliation(s)
- Yupu Liu
- Department of Chemistry, Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, iChEM and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
| | - Jinxiu Wang
- Department of Chemistry, Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, iChEM and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
| | - Wei Teng
- Department of Chemistry, Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, iChEM and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
| | - Chin-Te Hung
- Department of Chemistry, Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, iChEM and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
| | - Yunpu Zhai
- Green Catalysis Center, College of Chemistry, Zhengzhou University, Zhengzhou, Henan, 450001, P. R. China
| | - Dengke Shen
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, 230601, P. R. China
| | - Wei Li
- Department of Chemistry, Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, iChEM and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
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20
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Wang M, Luo Y, Wang T, Wan C, Pan L, Pan S, He K, Neo A, Chen X. Artificial Skin Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2003014. [PMID: 32930454 DOI: 10.1002/adma.202003014] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/03/2020] [Indexed: 05/23/2023]
Abstract
Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.
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Affiliation(s)
- Ming Wang
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Yifei Luo
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Wang
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Changjin Wan
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Liang Pan
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Shaowu Pan
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ke He
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Aden Neo
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Xiaodong Chen
- Innovative Center for Flexible Devices, Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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21
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Zeng T, Zou X, Wang Z, Yu G, Yang Z, Rong H, Zhang C, Xu H, Lin Y, Zhao X, Ma J, Zhu G, Liu Y. Zeolite-Based Memristive Synapse with Ultralow Sub-10-fJ Energy Consumption for Neuromorphic Computation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2006662. [PMID: 33738968 DOI: 10.1002/smll.202006662] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 01/08/2021] [Indexed: 06/12/2023]
Abstract
The development of neuromorphic computation faces the appreciable challenge of implementing hardware with energy consumption on the level of a femtojoule per synaptic event to be comparable with the energy consumption of human brain. Controllable ultrathin conductive filaments are needed to achieve such extremely low energy consumption in memristive synapses but their formation is difficult to control owing to their stochastic morphology and unexpected overgrowth. Herein, a zeolite-based memristive synapse is demonstrated for the first time, in which Ag exchange in the sub-nanometer pore closely resembles synaptic Ca2+ dynamics across biomembrane channel. Particularly, the confined ultrasmall pore and low Ag ion migration barrier give the zeolite-based memristive synapse ultralow energy consumption below 10 fJ per synaptic spike, on par with the biological counterpart. Experimental results reveal that the gradual memristive effect is attributed to the dimension modulation of Ag clusters. In addition to emulating inherent cognitive functions through electrical stimulations, the experience-dependent transition of short-term plasticity to long-term plasticity using a chemical modulation method is achieved by treating the initial Ag quantity as a learning experience. The proposed memristors can be used to develop highly efficient memristive neural networks and are considered as a candidate for application in neuromorphic computation.
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Affiliation(s)
- Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xiaoqin Zou
- Faculty of Chemistry, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Guangli Yu
- Faculty of Chemistry, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Zhi Yang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Huazhen Rong
- Faculty of Chemistry, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Chi Zhang
- Faculty of Chemistry, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Jiangang Ma
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Guangshan Zhu
- Faculty of Chemistry, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
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22
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Li B. Ordered Microporous Zeolite Film for Memristive Electronics. 2020 IEEE INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATION & SERVICES (HEALTHCOM) 2021. [DOI: 10.1109/healthcom49281.2021.9398985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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23
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Meng JL, Wang TY, He ZY, Chen L, Zhu H, Ji L, Sun QQ, Ding SJ, Bao WZ, Zhou P, Zhang DW. Flexible boron nitride-based memristor for in situ digital and analogue neuromorphic computing applications. MATERIALS HORIZONS 2021; 8:538-546. [PMID: 34821269 DOI: 10.1039/d0mh01730b] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradigms for improving the computing efficiency and avoiding high power consumption caused by extra data movement. However, memristors that can conduct digital memcomputing and neuromorphic computing simultaneously are limited by the difference in the information form between digital data and analogue data. In order to solve this problem, this paper proposes a flexible low-dimensional memristor based on boron nitride (BN), which has ultralow-power non-volatile memory characteristic, reliable digital memcomputing capabilities, and integrated ultrafast neuromorphic computing capabilities in a single in situ computing system. The logic-in-memory basis, including FALSE, material implication (IMP), and NAND, are implemented successfully. The power consumption of the proposed memristor per synaptic event (198 fJ) can be as low as biology (fJ level) and the response time (1 μs) of the neuromorphic computing is four orders of magnitude shorter than that of the human brain (10 ms), paving the way for wearable ultrahigh efficient next-generation in-memory computing architectures.
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Affiliation(s)
- Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
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24
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Zhou J, Li W, Chen Y, Lin YH, Yi M, Li J, Qian Y, Guo Y, Cao K, Xie L, Ling H, Ren Z, Xu J, Zhu J, Yan S, Huang W. A Monochloro Copper Phthalocyanine Memristor with High-Temperature Resilience for Electronic Synapse Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2006201. [PMID: 33354801 DOI: 10.1002/adma.202006201] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/15/2020] [Indexed: 06/12/2023]
Abstract
Memristors are considered to be one of the most promising device concepts for neuromorphic computing, in particular thanks to their highly tunable resistive states. To realize neuromorphic computing architectures, the assembly of large memristive crossbar arrays is necessary, but is often accompanied by severe heat dispassion. Organic materials can be tailored with on-demand electronic properties in the context of neuromorphic applications. However, such materials are more susceptible to heat, and detrimental effects such as thermally induced degradation directly lead to failure of device operation. Here, an organic memristive synapse formed of monochloro copper phthalocyanine, which remains operational and capable of memristive switching at temperatures as high as 300 °C in ambient air without any encapsulation, is demonstrated. The change in the electrical conductance is found to be a result of ion movement, closely resembling what takes place in biological neurons. Furthermore, the high viability of this approach is showcased by demonstrating flexible memristors with stable switching behaviors after repeated mechanical bending as well as organic synapses capable of emulating a trainable and reconfigurable memristor array for image information processing. The results set a precedent for thermally resilient organic synapses to impact organic neuromorphic devices in progressing their practicality.
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Affiliation(s)
- Jia Zhou
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Wen Li
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Ye Chen
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Yen-Hung Lin
- Clarendon Laboratory, Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PU, UK
| | - Mingdong Yi
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Jiayu Li
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Yangzhou Qian
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Yun Guo
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Keyang Cao
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Linghai Xie
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Haifeng Ling
- Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, China
| | - Zhongjie Ren
- State Key Laboratory of Chemical Resource Engineering, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jiangping Xu
- Key Lab of Materials Chemistry for Energy Conversion and Storage of Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Jintao Zhu
- Key Lab of Materials Chemistry for Energy Conversion and Storage of Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Shouke Yan
- State Key Laboratory of Chemical Resource Engineering, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics (FSCFE) and Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, 710072, China
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25
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Hou YX, Li Y, Zhang ZC, Li JQ, Qi DH, Chen XD, Wang JJ, Yao BW, Yu MX, Lu TB, Zhang J. Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing. ACS NANO 2021; 15:1497-1508. [PMID: 33372769 DOI: 10.1021/acsnano.0c08921] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.
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Affiliation(s)
| | | | | | - Jia-Qiang Li
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | | | | | | | | | | | | | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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26
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Yu J, Luo M, Lv Z, Huang S, Hsu HH, Kuo CC, Han ST, Zhou Y. Recent advances in optical and optoelectronic data storage based on luminescent nanomaterials. NANOSCALE 2020; 12:23391-23423. [PMID: 33227110 DOI: 10.1039/d0nr06719a] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.
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Affiliation(s)
- Jinbo Yu
- Institute of Microscale Optoelectronics, Shenzhen University, 3688 Nanhai Road, Shenzhen, 518060, P.R. China.
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27
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Yang JQ, Wang R, Ren Y, Mao JY, Wang ZP, Zhou Y, Han ST. Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2003610. [PMID: 33165986 DOI: 10.1002/adma.202003610] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/27/2020] [Indexed: 06/11/2023]
Abstract
The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.
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Affiliation(s)
- Jia-Qin Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ruopeng Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yi Ren
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jing-Yu Mao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Zhan-Peng Wang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
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28
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Meng Y, Li F, Lan C, Bu X, Kang X, Wei R, Yip S, Li D, Wang F, Takahashi T, Hosomi T, Nagashima K, Yanagida T, Ho JC. Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires. SCIENCE ADVANCES 2020; 6:6/46/eabc6389. [PMID: 33177088 PMCID: PMC7673733 DOI: 10.1126/sciadv.abc6389] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/23/2020] [Indexed: 05/27/2023]
Abstract
Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi-two-dimensional electron gases (quasi-2DEGs) in InGaO3(ZnO)3 superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca2+ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.
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Affiliation(s)
- You Meng
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Fangzhou Li
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Changyong Lan
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Xiuming Bu
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Xiaolin Kang
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Renjie Wei
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, P. R. China
| | - SenPo Yip
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, P. R. China
| | - Dapan Li
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Fei Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Tsunaki Takahashi
- Department of Applied Chemistry, School of Engineering, University of Tokyo, Tokyo 113-8654, Japan
| | - Takuro Hosomi
- Department of Applied Chemistry, School of Engineering, University of Tokyo, Tokyo 113-8654, Japan
| | - Kazuki Nagashima
- Department of Applied Chemistry, School of Engineering, University of Tokyo, Tokyo 113-8654, Japan
| | - Takeshi Yanagida
- Department of Applied Chemistry, School of Engineering, University of Tokyo, Tokyo 113-8654, Japan
- Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka 816-8580, Japan
| | - Johnny C Ho
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR.
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Centre for Functional Photonics, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, P. R. China
- Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka 816-8580, Japan
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29
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Gupta V, Lucarelli G, Castro-Hermosa S, Brown T, Ottavi M. Investigation of hysteresis in hole transport layer free metal halide perovskites cells under dark conditions. NANOTECHNOLOGY 2020; 31:445201. [PMID: 32679576 DOI: 10.1088/1361-6528/aba713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent research is a testimony to the fact that perovskite material based solar cells are most efficient since they exhibit high power conversion efficiency and low cost of fabrication. Various perovskite materials display hysteresis in their current-voltage characteristic which accounts for memory behaviour. In this paper, we demonstrate efficient non-volatile memory devices based on hybrid organic-inorganic perovskite (CH3NH3PbI3) as a resistive switching layer on a Glass/Indium Tin Oxide (ITO) substrate. Our perovskite solar cells have been developed over the fully solution processed electron transport layer (ETL) which is a combination of SnO2 and mesoporous (m)-TiO2 scaffold layers. Hysteresis behaviour was observed in the current-voltage analysis achieving high ratio of ON & OFF current under dark and ambient conditions. Proposed perovskite-based Glass/ITO/SnO2/m-TiO2/CH3NH3PbI3/Au device has a hole transport layer (HTL) free structure, which is mainly responsible for a large ratio of ON & OFF current. The presence of voids in the scaffold m-TiO2 layer are also accountable for increasing electron/hole path length which escalates the recombination rate at the surface of the ETL/perovskite interface resulting in large hysteresis in the I-V curve. This memristor device operates at a low energy due to SnO2 layer's higher electron mobility and wide energy band gap. Our experimental results also show the dependency of voltage scan range & rate of scanning on the hysteresis behaviour in dark conditions. This memristive behaviour of the proposed device depicts drift in hysteresis loop with respect to the number of cycles, which would have a significant impact in neuromorphic applications. Moreover, due to the identical fabrication process of the proposed perovskite-based memristor device and perovskite solar cells, this device could be integrated inside a photovoltaic array to work as a power-on-chip device, where generation and computation could be possible on the same substrate for memory and neuromorphic applications.
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Affiliation(s)
- Vishal Gupta
- Department of Electronic Engineering, University of Rome, Tor Vergata, Roma, Italy
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30
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Wang ZP, Wang Y, Yu J, Yang JQ, Zhou Y, Mao JY, Wang R, Zhao X, Zheng W, Han ST. Type-I Core-Shell ZnSe/ZnS Quantum Dot-Based Resistive Switching for Implementing Algorithm. NANO LETTERS 2020; 20:5562-5569. [PMID: 32579373 DOI: 10.1021/acs.nanolett.0c02227] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Core-shell semiconductor quantum dots (QDs) are one of the biggest nanotechnology successes so far. In particular, type-I QDs with straddling band offset possess the ability to enhance the charge carriers capturing which is useful for memory application. Here, the type-I core-shell QD-based bipolar resistive switching (RS) memory with anomalous multiple SET and RESET processes was demonstrated. The synergy and competition between space charge limited current conduction (arising from charge trapping in potential well of type-I QDs) and electrochemical metallization (ECM, originating from redox reaction of Ag electrode) process were employed for modulating the RS behavior. Through utilizing stochastic RS mechanisms in QD-based devices, four situations of RS behaviors can be classified into three states in Markov chain for implementing the application of a true random number generator. Furthermore, a 6 × 6 cross-bar array was demonstrated to realize the generation of random letters with case distinction.
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Affiliation(s)
- Zhan-Peng Wang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Yan Wang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Jinbo Yu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Jia-Qin Yang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Jing-Yu Mao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Ruopeng Wang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Xiaojin Zhao
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Wenhan Zheng
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P.R. China
| | - Su-Ting Han
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P.R. China
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31
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Wu C, Zhang Y, Zhou X, Li D, Park JH, An H, Sung S, Lin J, Guo T, Li F, Kim TW. Binary Electronic Synapses for Integrating Digital and Neuromorphic Computation in a Single Physical Platform. ACS APPLIED MATERIALS & INTERFACES 2020; 12:17130-17138. [PMID: 32174099 DOI: 10.1021/acsami.0c02145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
As a promising advanced computation technology, the integration of digital computation with neuromorphic computation into a single physical platform holds the advantage of a precise, deterministic, fast data process as well as the advantage of a flexible, paralleled, fault-tolerant data process. Even though two-terminal memristive devices have been respectively proved as leading electronic elements for digital computation and neuromorphic computation, it is difficult to steadily maintain both sudden-state-change and gradual-state-change in a single device due to the entirely different operating mechanisms. In this work, we developed a digital-analog compatible memristive device, namely, binary electronic synapse, through realizing controllable cation drift in a memristive layer. The devices feature nonvolatile binary memory as well as artificial neuromorphic plasticity with high operation endurance. With strong nonlinearity in switching dynamics, binary switching, neuromorphic plasticity, two-dimension information store, and trainable memory can be implemented by a single device.
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Affiliation(s)
- Chaoxing Wu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Yongai Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Xiongtu Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Dianlun Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Jae Hyeon Park
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 133-791, Korea
| | - Haoqun An
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 133-791, Korea
| | - Sihyun Sung
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 133-791, Korea
| | - Jintang Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Tailiang Guo
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Fushan Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Tae Whan Kim
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 133-791, Korea
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32
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Wan C, Cai P, Wang M, Qian Y, Huang W, Chen X. Artificial Sensory Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1902434. [PMID: 31364219 DOI: 10.1002/adma.201902434] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/08/2019] [Indexed: 06/10/2023]
Abstract
Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.
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Affiliation(s)
- Changjin Wan
- Innovative Center for Flexible Devices (iFLEX), Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Pingqiang Cai
- Innovative Center for Flexible Devices (iFLEX), Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ming Wang
- Innovative Center for Flexible Devices (iFLEX), Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Yan Qian
- Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts & Telecommunications (NUPT), 9 Wenyuan Road, Nanjing, 210023, China
| | - Wei Huang
- Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts & Telecommunications (NUPT), 9 Wenyuan Road, Nanjing, 210023, China
- Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaodong Chen
- Innovative Center for Flexible Devices (iFLEX), Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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33
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Wang Z, Zeng T, Ren Y, Lin Y, Xu H, Zhao X, Liu Y, Ielmini D. Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices. Nat Commun 2020; 11:1510. [PMID: 32198368 PMCID: PMC7083931 DOI: 10.1038/s41467-020-15158-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 02/12/2020] [Indexed: 11/08/2022] Open
Abstract
The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO3-x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.
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Affiliation(s)
- Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China
| | - Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China
| | - Yanyun Ren
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China.
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China.
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy.
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34
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Wu C, Kim TW, Park JH, Koo B, Sung S, Shao J, Zhang C, Wang ZL. Self-Powered Tactile Sensor with Learning and Memory. ACS NANO 2020; 14:1390-1398. [PMID: 31747246 DOI: 10.1021/acsnano.9b07165] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Fabrication of human-like intelligent tactile sensors is an intriguing challenge for developing human-machine interfaces. As inspired by somatosensory signal generation and neuroplasticity-based signal processing, intelligent neuromorphic tactile sensors with learning and memory based on the principle of a triboelectric nanogenerator are demonstrated. The tactile sensors can actively produce signals with various amplitudes on the basis of the history of pressure stimulations because of their capacity to mimic neuromorphic functions of synaptic potentiation and memory. The time over which these tactile sensors can retain the memorized information is alterable, enabling cascaded devices to have a multilevel forgetting process and to memorize a rich amount of information. Furthermore, smart fingers by using the tactile sensors are constructed to record a rich amount of information related to the fingers' current actions and previous actions. This intelligent active tactile sensor can be used as a functional element for artificial intelligence.
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Affiliation(s)
- Chaoxing Wu
- Department of Electronic and Computer Engineering , Hanyang University , Seoul 04763 , Republic of Korea
- College of Physics and Information Engineering , Fuzhou University , Fuzhou , 35000 , PR China
| | - Tae Whan Kim
- Department of Electronic and Computer Engineering , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jae Hyeon Park
- Department of Electronic and Computer Engineering , Hanyang University , Seoul 04763 , Republic of Korea
| | - Bonmin Koo
- Department of Electronic and Computer Engineering , Hanyang University , Seoul 04763 , Republic of Korea
| | - Sihyun Sung
- Department of Electronic and Computer Engineering , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jiajia Shao
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences , and National Center for Nanoscience and Technology (NCNST), Beijing 100083 , People's Republic of China
| | - Chi Zhang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences , and National Center for Nanoscience and Technology (NCNST), Beijing 100083 , People's Republic of China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences , and National Center for Nanoscience and Technology (NCNST), Beijing 100083 , People's Republic of China
- School of Materials Science and Engineering , Georgia Institute of Technology , Atlanta , Georgia 30332 , United States of America
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35
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Xing X, Chen M, Gong Y, Lv Z, Han ST, Zhou Y. Building memory devices from biocomposite electronic materials. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2020; 21:100-121. [PMID: 32165990 PMCID: PMC7054979 DOI: 10.1080/14686996.2020.1725395] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 01/30/2020] [Accepted: 01/31/2020] [Indexed: 05/05/2023]
Abstract
Natural biomaterials are potential candidates for the next generation of green electronics due to their biocompatibility and biodegradability. On the other hand, the application of biocomposite systems in information storage, photoelectrochemical sensing, and biomedicine has further promoted the progress of environmentally benign bioelectronics. Here, we mainly review recent progress in the development of biocomposites in data storage, focusing on the application of biocomposites in resistive random-access memory (RRAM) and field effect transistors (FET) with their device structure, working mechanism, flexibility, transient characteristics. Specifically, we discuss the application of biocomposite-based non-volatile memories for simulating biological synapse. Finally, the application prospect and development potential of biocomposites are presented.
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Affiliation(s)
- Xuechao Xing
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, P. R. China
| | - Meng Chen
- Institute for Advanced Study, Shenzhen University, Shenzhen, P. R. China
| | - Yue Gong
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, P. R. China
| | - Ziyu Lv
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, P. R. China
| | - Su-Ting Han
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, P. R. China
- CONTACT Ye Zhou Institute for Advanced Study, Shenzhen University, Shenzhen518060, P. R. China
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36
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He K, Liu Y, Wang M, Chen G, Jiang Y, Yu J, Wan C, Qi D, Xiao M, Leow WR, Yang H, Antonietti M, Chen X. An Artificial Somatic Reflex Arc. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1905399. [PMID: 31803996 DOI: 10.1002/adma.201905399] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 11/20/2019] [Indexed: 05/19/2023]
Abstract
The emulation of human sensation, perception, and action processes has become a major challenge for bioinspired intelligent robotics, interactive human-machine interfacing, and advanced prosthetics. Reflex actions, enabled through reflex arcs, are important for human and higher animals to respond to stimuli from environment without the brain processing and survive the risks of nature. An artificial reflex arc system that emulates the functions of the reflex arc simplifies the complex circuit design needed for "central-control-only" processes and becomes a basic electronic component in an intelligent soft robotics system. An artificial somatic reflex arc that enables the actuation of electrochemical actuators in response to the stimulation of tactile pressures is reported. Only if the detected pressure by the pressure sensor is above the stimulus threshold, the metal-organic-framework-based threshold controlling unit (TCU) can be activated and triggers the electrochemical actuators to complete the motion. Such responding mechanism mimics the all-or-none law in the human nervous system. As a proof of concept, the artificial somatic reflex arc is successfully integrated into a robot to mimic the infant grasp reflex. This work provides a unique and simplifying strategy for developing intelligent soft robotics, next-generation human-machine interfaces, and neuroprosthetics.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Yaqing Liu
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ming Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Geng Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ying Jiang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Jiancan Yu
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Changjin Wan
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Dianpeng Qi
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Meng Xiao
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Wan Ru Leow
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Hui Yang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Markus Antonietti
- Department of Colloid Chemistry, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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37
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Kumar M, Abbas S, Lee JH, Kim J. Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm. NANOSCALE 2019; 11:15596-15604. [PMID: 31403638 DOI: 10.1039/c9nr02027f] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The fundamental unit of the nervous system is a synapse, which is involved in transmitting information between neurons as well as learning, memory, and forgetting processes. Two-terminal memristors can fulfil most of these requirements; however, their poor dynamic changes in resistance to input electric stimuli remain an obstacle, which must be improved for accurate and quick information processing. Herein, we demonstrate the synaptic properties of ZnO-based memristors, which were significantly enhanced (∼340 times) by geometrical modulation due to the localized electric field enhancement. Specifically, by inserting Ag-nanowires and Ag-dots into the ZnO/Si interface, the resistive switching could be controlled from a digital to analog mode. A finite element simulation revealed that the presence of Ag could enhance the localized electric field, which in turn improved the migration of ionic species. Further, the device showed a variety of comprehensive synaptic functions, for instance, paired-pulse facilitation and transformation from short-term plasticity to long-term plasticity, including the Pavlovian associative learning process in a human brain. Our study presents a novel architecture to enhance the synaptic sensitivity, and its uses in practical applications, including the artificial learning algorithm.
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Affiliation(s)
- Mohit Kumar
- Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea.
| | - Sohail Abbas
- Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea.
| | - Jung-Ho Lee
- Department of Materials and Chemical Engineering, Hanyang University, Ansan, Kyunggido 15588, Korea.
| | - Joondong Kim
- Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea.
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38
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Ge C, Liu CX, Zhou QL, Zhang QH, Du JY, Li JK, Wang C, Gu L, Yang GZ, Jin KJ. A Ferrite Synaptic Transistor with Topotactic Transformation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1900379. [PMID: 30924206 DOI: 10.1002/adma.201900379] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 03/14/2019] [Indexed: 06/09/2023]
Abstract
Hardware implementation of artificial synaptic devices that emulate the functions of biological synapses is inspired by the biological neuromorphic system and has drawn considerable interest. Here, a three-terminal ferrite synaptic device based on a topotactic phase transition between crystalline phases is presented. The electrolyte-gating-controlled topotactic phase transformation between brownmillerite SrFeO2.5 and perovskite SrFeO3- δ is confirmed from the examination of the crystal and electronic structure. A synaptic transistor with electrolyte-gated ferrite films by harnessing gate-controllable multilevel conduction states, which originate from many distinct oxygen-deficient perovskite structures of SrFeOx induced by topotactic phase transformation, is successfully constructed. This three-terminal artificial synapse can mimic important synaptic functions, such as synaptic plasticity and spike-timing-dependent plasticity. Simulations of a neural network consisting of ferrite synaptic transistors indicate that the system offers high classification accuracy. These results provide insight into the potential application of advanced topotactic phase transformation materials for designing artificial synapses with high performance.
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Affiliation(s)
- Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chang-Xiang Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- Department of Physics, Capital Normal University, Beijing, 100048, China
| | - Qing-Li Zhou
- Department of Physics, Capital Normal University, Beijing, 100048, China
| | - Qing-Hua Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jian-Yu Du
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jian-Kun Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Lin Gu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Guo-Zhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kui-Juan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
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39
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Wang TY, Meng JL, He ZY, Chen L, Zhu H, Sun QQ, Ding SJ, Zhang DW. Atomic Layer Deposited Hf 0.5Zr 0.5O 2-based Flexible Memristor with Short/Long-Term Synaptic Plasticity. NANOSCALE RESEARCH LETTERS 2019; 14:102. [PMID: 30877593 PMCID: PMC6420527 DOI: 10.1186/s11671-019-2933-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/08/2019] [Indexed: 05/23/2023]
Abstract
Artificial synapses are the fundamental of building a neuron network for neuromorphic computing to overcome the bottleneck of the von Neumann system. Based on a low-temperature atomic layer deposition process, a flexible electrical synapse was proposed and showed bipolar resistive switching characteristics. With the formation and rupture of ions conductive filaments path, the conductance was modulated gradually. Under a series of pre-synaptic spikes, the device successfully emulated remarkable short-term plasticity, long-term plasticity, and forgetting behaviors. Therefore, memory and learning ability were integrated to the single flexible memristor, which are promising for the next-generation of artificial neuromorphic computing systems.
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Affiliation(s)
- Tian-Yu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - Jia-Lin Meng
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072 China
| | - Zhen-Yu He
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - Shi-Jin Ding
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433 China
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40
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Wang Y, Yang J, Wang Z, Chen J, Yang Q, Lv Z, Zhou Y, Zhai Y, Li Z, Han ST. Near-Infrared Annihilation of Conductive Filaments in Quasiplane MoSe 2 /Bi 2 Se 3 Nanosheets for Mimicking Heterosynaptic Plasticity. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2019; 15:e1805431. [PMID: 30653280 DOI: 10.1002/smll.201805431] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Indexed: 06/09/2023]
Abstract
It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre- and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high-performance artificial heterosynaptic systems. Nevertheless, the utilization of near-infrared (NIR) irradiation to act as a modulatory terminal for heterosynaptic plasticity emulation has not yet been realized. Here, an NIR resistive random access memory (RRAM) is reported, based on quasiplane MoSe2 /Bi2 Se3 heterostructure in which the anomalous NIR threshold switching and NIR reset operation are realized. Furthermore, it is shown that such an NIR irradiation can be employed as a modulatory terminal to emulate heterosynaptic plasticity. The reconfigurable 2D image recognition is also demonstrated by an RRAM crossbar array. NIR annihilation effect in quasiplane MoSe2 /Bi2 Se3 nanosheets may open a path toward optical-modulated in-memory computing and artificial retinal prostheses.
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Affiliation(s)
- Yan Wang
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jing Yang
- Hefei National Laboratory of Physical Sciences at the Microscale (HFNL), Department of Chemistry, Laboratory of Nanomaterials for Energy Conversion (LNEC), Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, P. R. China
| | - Zhanpeng Wang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jinrui Chen
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Qing Yang
- Hefei National Laboratory of Physical Sciences at the Microscale (HFNL), Department of Chemistry, Laboratory of Nanomaterials for Energy Conversion (LNEC), Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, P. R. China
| | - Ziyu Lv
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yongbiao Zhai
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Zongxiao Li
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
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41
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Dang B, Wu Q, Song F, Sun J, Yang M, Ma X, Wang H, Hao Y. A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors. NANOSCALE 2018; 10:20089-20095. [PMID: 30357252 DOI: 10.1039/c8nr07442a] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Physically transient electronic devices that can disappear on demand have great application prospects in the field of information security, implantable biomedical systems, and environment friendly electronics. On the other hand, the memristor-based artificial synapse is a promising candidate for new generation neuromorphic computing systems in artificial intelligence applications. Therefore, a physically transient synapse based on memristors is highly desirable for security neuromorphic computing and bio-integrated systems. Here, this is the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) behaviors. Moreover, to emulate the apoptotic process of biological neurons, the transient synapse devices can be dissolved completely in phosphate-buffered saline solution (PBS) or deionized (DI) water in 7 min. This work opens the route to security neuromorphic computing for smart security and defense electronic systems, as well as for neuro-medicine and implantable electronic systems.
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Affiliation(s)
- Bingjie Dang
- School of Advanced Materials and Nanotechnology, Key Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an, 710071, China.
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42
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Lv Z, Wang Y, Chen Z, Sun L, Wang J, Chen M, Xu Z, Liao Q, Zhou L, Chen X, Li J, Zhou K, Zhou Y, Zeng Y, Han S, Roy VAL. Phototunable Biomemory Based on Light-Mediated Charge Trap. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2018; 5:1800714. [PMID: 30250806 PMCID: PMC6145401 DOI: 10.1002/advs.201800714] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Indexed: 05/19/2023]
Abstract
Phototunable biomaterial-based resistive memory devices and understanding of their underlying switching mechanisms may pave a way toward new paradigm of smart and green electronics. Here, resistive switching behavior of photonic biomemory based on a novel structure of metal anode/carbon dots (CDs)-silk protein/indium tin oxide is systematically investigated, with Al, Au, and Ag anodes as case studies. The charge trapping/detrapping and metal filaments formation/rupture are observed by in situ Kelvin probe force microscopy investigations and scanning electron microscopy and energy-dispersive spectroscopy microanalysis, which demonstrates that the resistive switching behavior of Al, Au anode-based device are related to the space-charge-limited-conduction, while electrochemical metallization is the main mechanism for resistive transitions of Ag anode-based devices. Incorporation of CDs with light-adjustable charge trapping capacity is found to be responsible for phototunable resistive switching properties of CDs-based resistive random access memory by performing the ultraviolet light illumination studies on as-fabricated devices. The synergistic effect of photovoltaics and photogating can effectively enhance the internal electrical field to reduce the switching voltage. This demonstration provides a practical route for next-generation biocompatible electronics.
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Affiliation(s)
- Ziyu Lv
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
- Department of Materials Science and Engineering and State Key Laboratory of Millimeter WavesCity University of Hong KongTat Chee Avenue, KowloonHong Kong SAR999077China
| | - Yan Wang
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Zhonghui Chen
- State Key Laboratory of Advanced Technology for Materials Synthesis and ProcessingWuhan University of Technology122 Luoshi RoadWuhan430070P. R. China
| | - Long Sun
- State Key Laboratory of Transducer TechnologyShanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai200050China
| | - Junjie Wang
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Meng Chen
- Institute for Advanced StudyShenzhen UniversityShenzhen518060P. R. China
| | - Zhenting Xu
- Institute for Advanced StudyShenzhen UniversityShenzhen518060P. R. China
| | - Qiufan Liao
- College of Optoelectronic EngineeringShenzhen UniversityShenzhen518060P. R. China
| | - Li Zhou
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Xiaoli Chen
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Jieni Li
- College of Optoelectronic EngineeringShenzhen UniversityShenzhen518060P. R. China
| | - Kui Zhou
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Ye Zhou
- Institute for Advanced StudyShenzhen UniversityShenzhen518060P. R. China
| | - Yu‐Jia Zeng
- College of Optoelectronic EngineeringShenzhen UniversityShenzhen518060P. R. China
| | - Su‐Ting Han
- College of Electronic Science and TechnologyShenzhen UniversityShenzhen518060P. R. China
| | - Vellaisamy A. L. Roy
- Department of Materials Science and Engineering and State Key Laboratory of Millimeter WavesCity University of Hong KongTat Chee Avenue, KowloonHong Kong SAR999077China
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43
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Wang Y, Lv Z, Chen J, Wang Z, Zhou Y, Zhou L, Chen X, Han ST. Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2018; 30:e1802883. [PMID: 30063261 DOI: 10.1002/adma.201802883] [Citation(s) in RCA: 167] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 06/21/2018] [Indexed: 05/22/2023]
Abstract
Inspired by the biological neuromorphic system, which exhibits a high degree of connectivity to process huge amounts of information, photonic memory is expected to pave a way to overcome the von Neumann bottleneck for nonconventional computing. Here, a photonic flash memory based on all-inorganic CsPbBr3 perovskite quantum dots (QDs) is demonstrated. The heterostructure formed between the CsPbBr3 QDs and semiconductor layer serves as a basis for optically programmable and electrically erasable characteristics of the memory device. Furthermore, synapse functions including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity are emulated at the device level. The photonic potentiation and electrical habituation are implemented and the synaptic weight exhibits multiple wavelength response from 365, 450, 520 to 660 nm. These results may locate the stage for further thrilling novel advances in perovskite-based memories.
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Affiliation(s)
- Yan Wang
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ziyu Lv
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jinrui Chen
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Zhanpeng Wang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Li Zhou
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Xiaoli Chen
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- College of Electronic Science and Technology, Shenzhen University, Shenzhen, 518060, P. R. China
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