Issue

Vol. 13 (2021)

Memristive Artificial Synapses for Neuromorphic Computing

https://doi.org/10.1007/s40820-021-00618-2
Wen Huang
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Xuwen Xia
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Chen Zhu
College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Parker Steichen
Department of Materials Science and Engineering, University of Washington, Seattle, WA 98195‑2120, USA
Weidong Quan
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Weiwei Mao
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Jianping Yang
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Liang Chu
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China
Xing’ao Li
New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, People’s Republic of China

Corresponding Author: Xing’ao Li

iamxali@njupt.edu.cn

Nano-Micro Letters, Vol. 13 (2021), Article Number: 85

Abstract

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.


Highlights:


1 Synaptic devices that mimic synaptic functions are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.
2 The working mechanisms, progress, and application scenarios of synaptic devices based on electrical and optical signals are compared and analyzed.
3 The performances and future development of various synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Keywords

Synaptic devices Neuromorphic computing Electrical pulses Optical pulses Photoelectric synergetic effects
Huang, Wen, Xuwen Xia, Chen Zhu, Parker Steichen, Weidong Quan, Weiwei Mao, Jianping Yang, Liang Chu, and Xing’ao Li. 2021. “Memristive Artificial Synapses for Neuromorphic Computing”. Nano-Micro Letters 13 (March):85. https://doi.org/10.1007/s40820-021-00618-2.
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