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Review of resistive switching mechanisms for memristive neuromorphic devices

Chinese Physics B 2020年第9期29卷 1-14页

作者：杨蕊 State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Memristive devices have attracted intensive attention in developing hardware neuromorphic computing systems with high energy efficiency due to their simple structure,low power consumption,and rich switching dynamics resembling biological synapses and neurons in the last decades.Fruitful demonstrations have been achieved in memristive synapses neurons and neural networks in the last few years.Versatile dynamics are involved in the data processing and storage in biological neurons and synapses,which ask for carefully tuning the switching dynamics of the memristive emulators.Note that switching dynamics of the Memristive devices are closely related to switching mechanisms.Herein,from the perspective of switching dynamics modulations,the mainstream switching mechanisms including redox reaction with ion migration and electronic effect have been systemically reviewed.The approaches to tune the switching dynamics in the devices with different mechanisms have been described.Finally,some other mechanisms involved in neuromorphic computing are briefly introduced.

关键词： Memristive devices resistive switching mechanisms neuromorphic computing

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Flexible Memristive devices Based on Graphene Quantum-Dot Nanocomposites

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Computers, Materials & Continua 2022年第8期72卷 3283-3297页

作者： Sung Won Hwang Dae-Ki Hong Department of System Semiconductor Engineering SangMyung UniversityCheonan-si31066Korea

Artificial neural networks(ANNs)are attracting attention for their high performance in various fields,because increasing the network size improves its functioning.Since large-scale neural networks are difficult to implement on custom hardware,a two-dimensional(2D)structure is applied to an ANN in the form of a crossbar.We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips.The system is designed using two-dimensional structures,graphene quantum dots(GQDs)and graphene oxide(GO).Raman spectrum analysis results indicate a D-band of 1421 cm^(−1) that occurs in the disorder;band is expressed as an atomic characteristic of carbon in the sp2 hybridized structure.There is also a G-band of 1518 cm^(−1) that corresponds to the graphite structure.The G bands measured for RGO-GQDs present significant GQD edge-dependent shifts with position.To avoid an abruptly-formed conduction path,effect of barrier layer on graphene/ITO interface was investigated.We confirmed the variation in the nanostructure in the RGO-GQD layers by analyzing them using HR-TEM.After applying a negative bias to the electrode,a crystalline RGO-GQD region formed,which a conductive path.Especially,a synaptic array for a neuromorphic chip with GQDs applied was demonstrated using a crossbar array.

关键词： Memristive devices neuromorphic chip resistive RAM quantum dot graphene

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How could imperfect device properties influence the performances of spiking neural networks?

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Science China(Information Sciences) 2023年第8期66卷 233-242页

作者： Jingyang CHEN Zhihao WANG Tong WANG Heming HUANG Zheyuan SHAO Zhe WANG Xin GUO State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology

Spiking neural networks(SNNs) provide an efficient way to apply artificial intelligence systems on edge devices. Memristive devices with tunable conductance states can be used to emulate the functions of biological neurons and synapses and to build SNNs. In this work, fully-connected SNNs based on various Memristive devices are constructed, and a hardware-compatible spike-timing-dependent plasticity(STDP)learning rule is applied to train the SNNs. Strategies are designed to suppress the overfitting problem and improve the performance of the SNNs in the case of a small training set. However, the properties of Memristive devices are never perfect. The effects of imperfect device properties, e.g., asymmetric weight update, insufficient number of conductance states, and low on/off ratio, on the performance of the SNNs are elaborated.

关键词： oxides Memristive devices device properties spiking neural networks neuromorphic computing

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Solid-state non-volatile memories based on vdW heterostructure-based vertical-transport ferroelectric field-effect transistors

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Science China(Information Sciences) 2024年第6期67卷 198-206页

作者： Qiyu YANG Zheng-Dong LUO Fei XIAO Junpeng ZHANG Dawei ZHANG Dongxin TAN Xuetao GAN Yan LIU Zhufei CHU Yinshui XIA Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University School of Artificial Intelligence Xidian University School of Materials Science and Engineering University of New South Wales (UNSW) Sydney ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) University of New South Wales (UNSW) Sydney Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technologyand Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical University Faculty of Electrical Engineering and Computer Science Ningbo University

Driven by the explosive development of data-centric computation applications, it is becoming urgent to develop in-memory computing devices that are beyond the von Neumann architecture with an arrangement of separated logic and memory components. The transistor-type solid-state non-volatile memories,such as ferroelectric field-effect transistors(Fe FETs), have long been regarded as a competitive candidate for future in-memory computing architectures. However, the density scaling towards high-density arrays would require advanced Fe FETs with reduced footprints, which remains a great challenge so far. Here, a vertical-transport(VT) Fe FET that flips the charge transport channel perpendicular to the substrate plane is proposed, in which a ferroelectric gate and a van der Waals(vd W) heterojunction channel are vertically integrated, effectively reducing the device footprints. The proposed VT-Fe FET shows not only the robust binary non-volatile memory states but also several key synaptic functionalities at the device level. An artificial neural network with supervised learning was simulated based on the device conductance switching properties,showing excellent classification accuracy for the MNIST handwritten digits. These findings suggest that the proposed VT-Fe FET could offer a new solution for future non-volatile memories as well as more advanced neuromorphic systems.

关键词： vdW heterostructure non-volatile memory vertical-transport transistor ferroelectric field-effect transistor Memristive devices

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