The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore's Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.

Yu-Hao Wang;Tian-Cheng Gong;Ya-Xin Ding;Yang Li;Wei Wang;Zi-Ang Chen;Nan Du;Erika Covi;Matteo Farronato;Dniele Ielmini;Xu-Meng Zhang;Qing Luo

Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029;University of Chinese Academy of Sciences,Beijing 100049;University of Chinese Academy of Sciences, Beijing100049;Peng Cheng Laboratory, Shenzhen 518055;Institute for Solid State Physics, University of Jena, Jena 07743;Department of Quantum Detection, Leibniz Institute of Photonic Technology,Jena 07743;Nanoelectronic Materials Laboratory, Dresden 01187;Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133;University of Chinese Academy of Sciences, Beijing 100049;Frontier Institute of Chip and System, Fudan University, Shanghai 200433

电子科技学刊

[1]Yu-Hao Wang;Tian-Cheng Gong;Ya-Xin Ding;Yang Li;Wei Wang;Zi-Ang Chen;Nan Du;Erika Covi;Matteo Farronato;Dniele Ielmini;Xu-Meng Zhang;Qing Luo-.Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing)[J].电子科技学刊,2022(04):356-374

Memristors

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