IEEE Electron Device Letters, volume 39, issue 2, pages 308-311

An Artificial Neuron Based on a Threshold Switching Memristor

Xumeng Zhang ¹

Wei Wang ^{2, 3}

Qi Liu ¹

Zhao Xiaolong ¹

Jinsong Wei ¹

Rongrong Cao ¹

Zhihong Yao ¹

Xiaoli Zhu ¹

Feng Zhang ¹

Hangbing Lv ¹

Shibing Long ¹

Qing Luo ¹

Show full list: 12 authors

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University of Chinese Academy of Sciences, BeiJing, China |

College of Electronic Sciences and Engineering, National University of Defense Technology, Changsha, China |

Key Laboratory of Microelectronics Device and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China |

Publication type: Journal Article

Publication date: 2018-02-01

Institute of Electrical and Electronics Engineers (IEEE)

Journal: IEEE Electron Device Letters

scimago Q1

SJR: 1.250

CiteScore: 8.2

Impact factor: 4.1

ISSN: 07413106, 15580563

DOI: 10.1109/led.2017.2782752

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Electronic, Optical and Magnetic Materials

Electrical and Electronic Engineering

Abstract

Artificial neurons and synapses are critical units for processing intricate information in neuromorphic systems. Memristors are frequently engineered as artificial synapses due to their simple structures, gradually changing conductance and high-density integration. However, few studies have designed memristors as artificial neurons. In this letter, we demonstrate an integration-and-fire artificial neuron based on a Ag/SiO ₂ /Au threshold switching memristor. This neuron displays four critical features for action-potential-based computing: the all-or-nothing spiking of an action potential, threshold-driven spiking, a refractory period, and a strength-modulated frequency response. As a post-synaptic neuron, the designed neuron was demonstrated to be applicable to digit recognition. These results demonstrate that the developed artificial neuron can realize the basic functions of spiking neurons and has great potential for neuromorphic computing.

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