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arXiv 2024年

作者： Chen, Zihao Xiao, Zhili Akl, Mahmoud Leugring, Johannes Olajide, Omowuyi Malik, Adil Dennler, Nik Harper, Chad Bose, Subhankar Gonzalez, Hector A. Eshraghian, Jason Pignari, Riccardo Urgese, Gianvito Andreou, Andreas G. Shankar, Sadasivan Mayr, Christian Cauwenberghs, Gert Chakrabartty, Shantanu Department of Electrical and Systems Engineering Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States Department of Bioengineering University of California 9500 Gilman Dr La Jolla San DiegoCA92093 United States SpiNNcloud Systems GmbH Freibergerstr. 37 Dresden01067 Germany Department of Electrical and computer engineering Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Electrical and Electronic Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom International Centre for Neuromorphic Engineering Western Sydney University Penrith Second Ave KingswoodNSW2747 Australia Department of Physics University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Department of Electrical and Computer Engineering University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Politecnico di Torino Address: Corso Duca degli Abruzzi 24 Torino10129 Italy Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Biocomputation Group University of Hertfordshire Exhibition Rd LondonSW7 2AZ United Kingdom Materials Science and Engineering Stanford University 450 Jane Stanford Way StanfordCA94305 United States Highly-Parallel VLSI-Systems and Neuro-Microelectronics Technische Universität Dresden Mommsenstraße 12 Dresden01069 Germany Scads.AI: Center for scalable data analytics and artificial intelligence Strehlener Street 12 14 Dresden01069 Germany

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN). The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing (SA) dynamics onto a network of integrate-and-fire (IF) neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer which replicates the optimal escape mechanism and convergence of SA, particularly at low temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved various benchmark MAX-CUT combinatorial optimization problems. Across multiple runs, NeuroSA consistently generates solutions that approach the state-of-the-art level with high accuracy (greater than 99%), and without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform. © 2024, CC BY-NC-SA.

关键词： Simulated annealing

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Numerical analysis of high-efficiency lead-free perovskite solar cell with NiO as hole transport material and PCBM as electron transport material

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CSI Transactions on ICT 2020年第2期8卷 111-116页

作者： T. R. Lenka A. C. Soibam K. Dey T. Maung F. Lin Microelectronics and VLSI Design Group Department of Electronics and Communication Engineering National Institute of Technology Silchar Silchar India Cavendish Laboratory Department of Physics University of Cambridge Cambridge UK Solar Energy Research Institute of Singapore National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this work a lead free perovskite solar cell structure is proposed with NiO as the hole transport material (HTM), CH3NH3SnI3 as the perovskite absorber material and PCBM (phenyl C61 butyric acid methyl ester) as the electron transport material (ETM). Numerical analysis of the designed solar cell is performed using Solar Cell Capacitance Simulator (SCAPS-1D) program. The power conversion efficiency (PCE) of the optimized device stack is found to be above 29% with Voc = 0.98 V, Jsc = 34.86 mA/cm2, FF = 85.64%. The lead free perovskite solar cell with different HTM and ETM may be investigated for high PCE.

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Ruddlesden-Popper chalcogenides push the limit of mechanical stiffness and glass-like thermal conductivity in single crystals

arXiv

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arXiv 2023年

作者： Hoque, Md Shafkat Bin Hoglund, Eric R. Zhao, Boyang Bao, De-Liang Zhou, Hao Thakur, Sandip Osei-Agyemang, Eric Hattar, Khalid Scott, Ethan A. Surendran, Mythili Tomko, John A. Gaskins, John T. Aryana, Kiumars Makarem, Sara Alwen, Adie Hodge, Andrea Balasubramanian, Ganesh Giri, Ashutosh Feng, Tianli Hachtel, Jordan A. Ravichandran, Jayakanth Pantelides, Sokrates T. Hopkins, Patrick E. Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering University of Utah Salt Lake CityUT84112 United States Department of Mechanical Industrial and Systems Engineering University of Rhode Island KingstonRI02881 United States Department of Materials Design and Innovation University at Buffalo The State University of New York BuffaloNY14260 United States Department of Mechanical Engineering and Mechanics Lehigh University 19 Memorial Drive West BethlehemPA18015 United States Nuclear Engineering University of Tennessee KnoxvilleTN37996 United States Sandia National Laboratories AlbuquerqueNM87185 United States Laser thermal analysis CharlottesvilleVA22902 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States

Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded single crystals in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline Ruddlesden-Popper Ban+1ZrnS3n+1, n = 2,3, which are derivatives of BaZrS3. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of "mechanically stiff phonon glasses". © 2023, CC BY.

关键词： Single crystals

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Generalized Scaling Law for Exciton Binding Energy in Two-Dimensional Materials

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Physical Review Applied 2020年第6期13卷 064062-064062页

作者： S. Ahmad M. Zubair O. Jalil M. Q. Mehmood U. Younis X. Liu K. W. Ang L. K. Ang Electrical Engineering Department Information Technology University (ITU) of the Punjab Lahore 54000 Pakistan NanoTech Lab Electrical Engineering Department Information Technology University (ITU) of the Punjab Lahore 54000 Pakistan College of Materials Science and Engineering Shenzhen Key Laboratory of Microscale Optical Information Technology Chinese Engineering and Research Institute of Microelectronics Shenzhen University 3688 Nanhai Avenue Shenzhen 518060 People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Science and Math Cluster Singapore University of Technology and Design (SUTD) 8 Somapah Road Singapore 487372 Singapore

Binding energy calculation in two-dimensional (2D) materials is crucial in determining their electronic and optical properties pertaining to enhanced Coulomb interactions between charge carriers due to quantum confinement and reduced dielectric screening. Based on full solutions of the Schrödinger equation in a screened hydrogen model with a modified Coulomb potential (1/rβ−2), we present a generalized and analytical scaling law for the exciton binding energy, Eβ=E0(aβb+c)(μ/ϵ2), where β is a fractional-dimension parameter that accounts for the reduced dielectric screening. The model is able to provide accurate binding energies, benchmarked using the reported Bethe-Salpeter equation and experimental data, for 58 monolayer 2D and eight bulk materials, respectively, through β. For a given material, β is varied from β=3 for bulk three-dimensional materials to a value lying in the range 2.55–2.7 for 2D monolayer materials. With βmean=2.625, our model improves the average relative mean square error by a factor of 3 in comparison to existing models. The results can be used for Coulomb engineering of exciton binding energies in the optimal design of 2D materials.

关键词： Excitons Optoelectronics 2-dimensional systems Quantum wells Semiconductors Scaling methods

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Echo state graph neural networks with analogue random resistor arrays

arXiv

引用

arXiv 2021年

作者： Wang, Shaocong Li, Yi Wang, Dingchen Zhang, Woyu Chen, Xi Dong, Danian Wang, Songqi Zhang, Xumeng Lin, Peng Gallicchio, Claudio Xu, Xiaoxin Liu, Qi Cheng, Kwang-Ting Wang, Zhongrui Shang, Dashan Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai 200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Recent years have witnessed an unprecedented surge of interest, from social networks to drug discovery, in learning representations of graph-structured data. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conventional digital hardware, including von Neumann bottleneck incurred by physically separated memory and processing units, slowdown of Moore's law due to transistor scaling limit, and expensive training cost. Here we present a novel hardware-software co-design, the random resistor array-based echo state graph neural network, which addresses these challenges. The random resistor arrays not only harness low-cost, nanoscale and stackable resistors for highly efficient in-memory computing using simple physical laws, but also leverage the intrinsic stochasticity of dielectric breakdown to implement random projections in hardware for an echo state network that effectively minimizes the training cost thanks to its fixed and random weights. The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 34.2×, 93.2×, and 570.4× improvement of energy efficiency and 98.27%, 99.46%, and 95.12% reduction of training cost compared to conventional graph learning on digital hardware, respectively, which may pave the way for the next generation AI system for graph learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Geometric Filterless Photodetectors for Mid-infrared Spin Light

arXiv

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arXiv 2022年

作者： Wei, Jingxuan Chen, Yang Li, Ying Li, Wei Xie, Junsheng Lee, Chengkuo Novoselov, Kostya S. Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei230026 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Haining314400 China GPL Photonics Lab State Key Laboratory of Applied Optics Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Center for Intelligent Sensors and MEMS National University of Singapore Singapore117608 Singapore Institute for Functional Intelligent Materials National University of Singapore Singapore117544 Singapore

Free-space circularly polarized light (CPL) detection, requiring polarizers and waveplates, has been well established, while such spatial degree of freedom is unfortunately absent in integrated on-chip optoelectronics. So far, those reported filterless CPL photodetectors suffer from the intrinsic small discrimination ratio, vulnerability to the non-CPL field components, and low responsivity. Here, we report a distinct paradigm of geometric photodetectors in mid-infrared exhibiting colossal discrimination ratio, close-to-perfect CPL-specific response, a zero-bias responsivity of 392 V/W at room temperature, and a detectivity of ellipticity down to 0.03o Hz−1/2. Our approach employs plasmonic nanostructures array with judiciously designed symmetry, assisted by graphene ribbons to electrically read their near-field optical information. This geometry-empowered recipe for infrared photodetectors provides a robust, direct, strict, and high-quality solution to on-chip filterless CPL detection and unlocks new opportunities for integrated functional optoelectronic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Photodetectors

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Continuous-time digital twin with analog memristive neural ordinary differential equation solver

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Science Advances 2025年第22期11卷 eadr7571页

作者： Hegan Chen Jichang Yang Jia Chen Songqi Wang Shaocong Wang Dingchen Wang Xinyu Tian Yifei Yu Xi Chen Yinan Lin Qifan Zhu Yangu He Xiaoshan Wu Yi Li Xinyuan Zhang Ning Lin Meng Xu Xumeng Zhang Xiaojuan Qi Zhongrui Wang Han Wang Dashan Shang Qi Liu Kwang-Ting Cheng Ming Liu Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong China. School of Microelectronics Southern University of Science and Technology Shenzhen 518055 China. Center for Advanced Semiconductors and Integrated Circuits the University of Hong Kong Hong Kong China. Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong China. School of Integrated Circuits Hubei Key Laboratory for Advanced Memories Huazhong University of Science and Technology Wuhan 430074 China. State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai 200433 China. State Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China. University of Chinese Academy of Sciences Beijing 100049 China.

Digital twins, which replicate real-world entities through computational models, are transforming manufacturing and automation. While recent advances in machine learning have enabled data-driven digital twin development using discrete-time data and finite-depth models on digital hardware, these approaches face significant limitations. They struggle to capture continuous-time dynamics and model complex systems, and suffer from substantial time and energy overheads due to physically separated storage and processing as well as frequent analog-digital (A/D) conversions. Here, we propose a memristive neural ordinary differential equation (ODE) solver for digital twins. Our approach is intrinsically time-continuous using infinite-depth neural networks to model complex dynamics. Fully analog memristor arrays collocate storage and computation, addressing the von Neumann bottleneck and reducing A/D conversion requirements. We experimentally validate our solver on digital twins of HP variable-resistor model and Lorenz96 dynamics, demonstrating a 166.5-fold/369.3-fold speedup and a 499.0-fold/673.9-fold improvement in energy efficiency, respectively. This work paves the way to future digital twins for Industry 4.0.

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Consistent Discretization of a Class of Predefined-Time Stable systems

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IFAC-PapersOnLine 2020年第2期53卷 628-633页

作者： Esteban Jiménez-Rodríguez Rodrigo Aldana-López Juan D. Sánchez-Torres David Gómez-Gutiérrez Alexander G. Loukianov Relativity6 Inc. Computer Science & Artificial Intelligence Lab Jalisco 45040 México Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza 50009 España Research Laboratory on Optimal Design Devices and Advanced Materials -OPTIMA- Department of Mathematics and Physics ITESO Jalisco 45604 México Multi-agent autonomous systems lab Intel Labs Intel Tecnología de México Jalisco 45019 Mexico Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Jalisco 45138 Mexico Department of Electrical Engineering Cinvestav-Guadalajara Jalisco 45019 México

As the main contribution, this document provides a consistent discretization of a class of fixed-time stable systems, namely predefined-time stable systems. In the unperturbed case, the proposed approach allows obtaining not only a consistent but exact discretization of the considered class of predefined-time stable systems, whereas in the perturbed case, the consistent discretization preserves the predefined-time stability property. All the results are validated through simulations and compared with the conventional explicit Euler scheme, highlighting the advantages of this proposal.

关键词： Predefined-time stability Discrete-time systems Digital implementation Stability of nonlinear systems Fixed-time stability

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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