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Redox memristors with volatile threshold switching behavior for neuromorphic computing

Authors :
Yu-Hao Wang
Tian-Cheng Gong
Ya-Xin Ding
Yang Li
Wei Wang
Zi-Ang Chen
Nan Du
Erika Covi
Matteo Farronato
Daniele Ielmini
Xu-Meng Zhang
Qing Luo
Source :
Journal of Electronic Science and Technology, Vol 20, Iss 4, Pp 100177- (2022)
Publication Year :
2022
Publisher :
KeAi Communications Co., Ltd., 2022.

Abstract

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.

Details

Language :
English
ISSN :
2666223X
Volume :
20
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Electronic Science and Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.6e756df88bb548abbc75c9182ec07872
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jnlest.2022.100177