Redox memristors with volatile threshold switching behavior for neuromorphic computing

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
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引用次数: 1

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.

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神经形态计算中具有挥发性阈值开关行为的氧化还原记忆电阻器
脉冲神经网络(SNN)受到人类大脑的密切启发,是最强大的平台之一,可以在集成系统中使用传统或新兴的电子设备在硬件上实现高效、低成本和鲁棒的神经形态计算。在硬件实现中,人工尖峰神经元的构建是构建整个系统的基础。然而,随着摩尔定律的放缓,传统的互补金属氧化物半导体(CMOS)技术正在逐渐衰落,无法满足日益增长的神经形态计算需求。此外,由于CMOS器件的生物合理性有限,现有的人工神经元电路非常复杂。具有易失性阈值开关(TS)行为和丰富动态特性的忆阻器是模拟CMOS技术之外的生物尖峰神经元和构建高效神经形态系统的有希望的候选器件。本文对snn的基础知识进行了综述。此外,我们回顾了基于TS记忆器的神经元及其系统的实现,并指出了在系统演示中从器件到电路应进一步考虑的挑战。我们希望这一综述能够为记忆电阻器神经形态计算的未来发展提供线索和帮助。
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来源期刊
Journal of Electronic Science and Technology
Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
0.00%
发文量
1362
审稿时长
99 days
期刊介绍: JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.
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