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Preparation of MXene-based hybrids and their application in neuromorphic devices

International Journal of Extreme Manufacturing 2024年第2期6卷 164-188页

作者： Zhuohao Xiao Xiaodong Xiao Ling Bing Kong Hongbo Dong Xiuying Li Bin He Shuangchen Ruan Jianpang Zhai Kun Zhou Qin Huang Liang Chu School of Materials Science and Engineering Jingdezhen Ceramic UniversityJingdezhen 333403JiangxiPeople’s Republic of China School of Mechanical and Vehicle Engineering Linyi UniversityLinyi 276000ShandongPeople’s Republic of China College of New Materials and New Energies Shenzhen Technology UniversityShenzhen 518118GuangdongPeople’s Republic of China School of Mechanical&Aerospace Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore Ningbo Institute of Materials Engineering and Technology Chinese Academy of ScienceNingbo 315201ZhejiangPeople’s Republic of China Institute of Carbon Neutrality and New Energy&School of Electronics and Information Hangzhou Dianzi UniversityHangzhou 310018ZhejiangPeople’s Republic of China

The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption,making it difficult to meet the computing needs of artificial intelligence(AI).neuromorphic computing systems,with massively parallel computing capability and low power consumption,have been considered as an ideal option for data storage and AI computing in the future.Memristor,as the fourth basic electronic component besides resistance,capacitance and inductance,is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure,continuously adjustable conductivity state,ultra-low power consumption,high switching speed and compatibility with existing CMOS technology.The memristors with applying MXene-based hybrids have attracted significant attention in recent years.Here,we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence.We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices.Finally,the future prospects and directions of MXene-based memristors are briefly described.

关键词： MXene etching fabrication memristor neuromorphic

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Artificial Intelligence Meets Flexible Sensors:Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

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Nano-Micro Letters 2024年第1期16卷 235-273页

作者： Tianming Sun Bin Feng Jinpeng Huo Yu Xiao Wengan Wang Jin Peng Zehua Li Chengjie Du Wenxian Wang Guisheng Zou Lei Liu Department of Mechanical Engineering State Key Laboratory of Tribology in Advanced EquipmentKey Laboratory for Advanced Manufacturing by Materials Processing TechnologyMinistry of Education of PR ChinaTsinghua UniversityBeijing 100084People’s Republic of China College of Materials Science and Engineering Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024People’s Republic of China

The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human society.As an essential component that bridges the physical world and digital signals,flexible sensors are evolving from a single sensing element to a smarter system,which is capable of highly efficient acquisition,analysis,and even perception of vast,multifaceted data.While challenging from a manual perspective,the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm(machine learning)and the framework(artificial synapses)level.This review presents the recent progress of the emerging AI-driven,intelligent flexible sensing systems.The basic concept of machine learning and artificial synapses are introduced.The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed,which significantly advances the applications such as flexible sensory systems,soft/humanoid robotics,and human activity monitoring.As two of the most profound innovations in the twenty-first century,the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

关键词： Flexible electronics Wearable electronics neuromorphic Memristor Deep learning

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Recent advances in bioinspired vision systems with curved imaging structures

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Rare Metals 2024年第4期43卷 1407-1434页

作者： Ze-Ping He Xun Han Wen-Qiang Wu Zhang-Sheng Xu Cao-Feng Pan CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-Nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of Sciences School of Nanoscience and Technology University of Chinese Academy of Sciences ZJU-Hangzhou Global Scientific and Technological Innovation Center School of Micro-Nano ElectronicsZhejiang University Institute of Microscale Optoelectronics Shenzhen University

Limited by the planar imaging structure,the commercial camera needs to introduce additional optical elements to compensate for the curved focal plane to match the planar image sensor.This results in a complex and bulky structure.In contrast,biological eyes possess a simple and compact structure due to their curved imaging structure that can directly match with the curved focal plane.Inspired by the structures and functions of biological eyes,curved vision systems not only improve the image quality,but also offer a variety of advanced functions.Here,we review the recent advances in bioinspired vision systems with curved imaging structures.Specifically,we focus on their applications in implementing different functions of biological eyes,as well as the emerging curved neuromorphic imaging systems that incorporate bioinspired optical and neuromorphic processing technologies.In addition,the challenges and opportunities of bioinspired curved imaging systems are also discussed.

关键词： Bioinspired Curved vision system Compound eye Chambered eye neuromorphic

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Artificial optoelectronic synapse based on spatiotemporal irradiation to source-sharing circuitry of synaptic phototransistors

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InfoMat 2024年第2期6卷 131-141页

作者： Seungho Song Changsoon Choi Jongtae Ahn Je-Jun Lee Jisu Jang Byoung-Soo Yu Jung Pyo Hong Yong-Sang Ryu Yong-Hoon Kim Do Kyung Hwang Center for Opto-Electronic Materials and Devices Post-Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)SeoulRepublic of Korea School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Center for Spintronics Post-Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)SeoulRepublic of Korea Division of Nanoscience&Technology KIST SchoolUniversity of Science and Technology(UST)SeoulRepublic of Korea School of Biomedical Engineering Korea UniversitySeoulRepublic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoulRepublic of Korea

To overcome the intrinsic inefficiency of the von Neumann architecture,neuromorphic devices that perform analog vector–matrix multiplication have been highlighted for achieving power-and time-efficient data processing.In particular,artificial synapses,of which conductance should be programmed to represent the synaptic weights of the artificial neural network,have been intensively researched to realize neuromorphic devices.Here,inspired by excitatory and inhibitory synapses,we develop an artificial optoelectronic synapse that shows both potentiation and depression characteristics triggered only by optical inputs.The design of the artificial optoelectronic synapse,in which excitatory and inhibitory synaptic phototransistors are serially connected,enables these characteristics by spatiotemporally irradiating the phototransistor channels with optical pulses.Furthermore,a negative synaptic weight can be realized without the need for electronic components such as comparators.With such attributes,the artificial optoelectronic synapse is demonstrated to classify three digits with a high recognition rate(98.3%)and perform image preprocessing via analog vector-matrix multiplication.

关键词： amorphous oxide semiconductor analog processing artificial synapse neuromorphic optoelectronics

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neuromorphic sensory computing

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Science China(Information Sciences) 2022年第4期65卷 114-127页

作者： Tianqing WAN Sijie MA Fuyou LIAO Lingwei FAN Yang CHAI Department of Applied Physics The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute

The number of sensory nodes in the Internet of everything continues to increase rapidly and generate massive data. The generated information from sensory nodes is much larger than the total collective human sensory throughput. It is quite challenging to send all of the data produced at sensory terminals to the“Cloud” computation center, especially for those time-delay sensitive applications. This situation demands a dramatic increase in the computation near or inside sensory networks. Inspired by biological sensory systems with a high data compression ratio, neuromorphic sensory computing provides a way to efficiently acquire and process a large volume of data from complex environments. Researchers have been investigating emerging materials, devices, circuits, and computing architectures to implement an artificial sensory system with high energy efficiency, speed, and density. Here we summarize the important features of biological systems and their hardware implementations. Electrons and photons are two representative information carriers, in which electron carrier allows high integration density for complex computing and photon carrier has high connectivity, high speed, wide bandwidth, and low power consumption. We overview the electronic and optical neuromorphic sensory computing and hybrid opto-electronic sensory computing, and present advances on multimodal sensory computing and their potential challenges.

关键词： neuromorphic sensory computing multimodal sensory computing electronic sensory computing optical sensory computing

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Astrometric calibration and source characterisation of the latest generation neuromorphic event-based cameras for space imaging

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Astrodynamics 2023年第4期7卷 415-443页

作者： Nicholas Owen Ralph Alexandre Marcireau Saeed Afshar Nicholas Tothill Andre van Schaik Gregory Cohen International Centre for neuromorphic Systems MARCS Institute for BrainBehaviour and DevelopmentWestern Sydney UniversityKingswood 2747NSWAustralia

As an emerging approach to space situational awareness and space imaging,the practical use of an event-based camera(EBC)in space imaging for precise source analysis is still in its infancy.The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space imaging systems and advance the capabilities of event-based tracking systems with improved target measurement models.Moreover,for event measurements to be meaningful,a framework must be investigated for EBC calibration to project events from pixel array coordinates in the image plane to coordinates in a target resident space object’s reference frame.In this paper,the traditional techniques of conventional astronomy are reconsidered to properly utilise the EBC for space imaging and space situational awareness.This paper presents the techniques and systems used for calibrating an EBC for reliable and accurate measurement acquisition.These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks.By calibrating sources detected using the EBC,the spatiotemporal characteristics of detected sources or“event sources”can be related to the photometric characteristics of the underlying astrophysical objects.Finally,these characteristics are analysed to establish a foundation for principled processing and observing techniques which appropriately exploit the capabilities of the EBC.

关键词： neuromorphic event-based camera(EBC) space imaging space situational awareness(SSA) astronomy

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neuromorphic vision chips

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Science China(Information Sciences) 2018年第6期61卷 119-135页

作者： Nanjian WU State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

The paper reviews the progress of neuromorphic vision chip research in decades. It focuses on two kinds of the neuromorphic vision chips: frame-driven(FD) and event-driven(ED) vision chips. The FD and ED vision chips are very different from each other in system architecture, image sensing, image information coding, image processing algorithm, design methodology. The vision chips can overcome serial data transmission and processing bottlenecks in traditional image processing systems. They can perform the high speed image capture and real-time image processing operations. This paper selects two typical chips from the two kinds of vision chips, respectively, and introduces their architectures, image sensing schemes, image processing processors and system operation. The FD neuromorphic reconfigurable vision chip comprises a high speed image sensor, a processing element array and self-organizing map neural network. The FD vision chip has the advantages in image resolution, static object detection, time-multiplex image processing, and chip area. The ED neuromorphic vision chip system is based on address-event-representation image sensor and event-driven multi-kernel convolution network. The ED vision chip has the advantages in fast sensing, low communication bandwidth, brain-like processing, and high energy efficiency. Finally, this paper discusses the architecture and the challenges of the future neuromorphic vision chip and indicates that the reconfigurable vision chip with left-and right-brain functions integrated in the three dimensional(3 D) large-scale integrated circuit(LSI) technology becomes a trend of the research on the vision chip.

关键词： neuromorphic vison chip frame-driven address-event-representation(AER) event-driven convolution neural network image sensor image processing

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Two-dimensional materials for synaptic electronics and neuromorphic systems

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Science Bulletin 2019年第15期64卷 1056-1066页

作者： Shuiyuan Wang David Wei Zhang Peng Zhou State Key Laboratory of ASIC and System School of Microelectronics Fudan University

Synapses in biology provide a variety of functions for the neural system. Artificial synaptic electronics that mimic the biological neuron functions are basic building blocks and developing novel artificial synapses is essential for neuromorphic computation. Inspired by the unique features of biological synapses that the basic connection components of the nervous system and the parallelism, low power consumption, fault tolerance, self-learning and robustness of biological neural systems, artificial synaptic electronics and neuromorphic systems have the potential to overcome the traditional von Neumann bottleneck and create a new paradigm for dealing with complex problems such as pattern recognition, image classification, decision making and associative learning. Nowadays, two-dimensional(2 D) materials have drawn great attention in simulating synaptic dynamic plasticity and neuromorphic computing with their unique properties. Here we describe the basic concepts of bio-synaptic plasticity and learning, the 2 D materials library and its preparation. We review recent advances in synaptic electronics and artificial neuromorphic systems based on 2 D materials and provide our perspective in utilizing 2 D materials to implement synaptic electronics and neuromorphic systems in hardware.

关键词： Artificial synaptic electronics neuromorphic computation 2D materials Synaptic plasticity Hebbian learning

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Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

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Nano-Micro Letters 2023年第5期15卷 236-253页

作者： Ji Hyun Baek Kyung Ju Kwak Seung Ju Kim Jaehyun Kim Jae Young Kim In Hyuk Im Sunyoung Lee Kisuk Kang Ho Won Jang Department of Materials Science and Engineering Research Institute of Advanced MaterialsSeoul National UniversitySeoul 08826Republic of Korea Advanced Institute of Convergence Technology Seoul National UniversitySuwon 16229Korea

Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of nondistributed weight updates owing to stable ion migrations.However,the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems.Meanwhile,achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging.Here,two-terminal Au/LixCoO_(2)/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks.The Au/LixCoO_(2)/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO_(2)films.The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike,resulting in improved weight control functionality.Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity,symmetricity,and dynamic range.Notably,the LixCoO_(2)-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error.These impressive performances suggest the vertical two-terminal Au/LixCoO_(2)/Pt artificial synapses as promising candidates for hardware neural networks.

关键词： Artificial synapse neuromorphic Li-based Two-terminal Synaptic plasticity

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Optoelectronic memristor for neuromorphic computing

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Chinese Physics B 2020年第4期29卷 15-30页

作者：薛武红次红娟许小红刘刚 Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials ScienceShanxi Normal UniversityLinfen 041004China School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China College of Chemistry and Molecular Engineering Zhengzhou UniversityZhengzhou 450001China

With the need of the internet of things,big data,and artificial intelligence,creating new computing architecture is greatly desired for handling data-intensive tasks.Human brain can simultaneously process and store information,which would reduce the power consumption while improve the efficiency of computing.Therefore,the development of brainlike intelligent device and the construction of brain-like computation are important breakthroughs in the field of artificial intelligence.Memristor,as the fourth fundamental circuit element,is an ideal synaptic simulator due to its integration of storage and processing characteristics,and very similar activities and the working mechanism to synapses among neurons which are the most numerous components of the brains.In particular,memristive synaptic devices with optoelectronic responding capability have the benefits of storing and processing transmitted optical signals with wide bandwidth,ultrafast data operation speed,low power consumption,and low cross-talk,which is important for building efficient brain-like computing networks.Herein,we review recent progresses in optoelectronic memristor for neuromorphic computing,including the optoelectronic memristive materials,working principles,applications,as well as the current challenges and the future development of the optoelectronic memristor.

关键词： memristor optoelectronic neuromorphic computing

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