Institute of Electrical and Electronics Engineers (IEEE)

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

Hide authors affiliations Show authors affiliations: 3 affiliations

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)

IEEE Electron Device Letters

scimago Q1

wos Q2

SJR: 1.150

CiteScore: 7.6

Impact factor: 4.5

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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GOST |

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GOST Copy

Zhang X. et al. An Artificial Neuron Based on a Threshold Switching Memristor // IEEE Electron Device Letters. 2018. Vol. 39. No. 2. pp. 308-311.

GOST all authors (up to 50) Copy

Zhang X., Wang W., Liu Q., Xiaolong Z., Wei J., Cao R., Yao Z., Zhu X., Zhang F., Lv H., Long S., Luo Q. An Artificial Neuron Based on a Threshold Switching Memristor // IEEE Electron Device Letters. 2018. Vol. 39. No. 2. pp. 308-311.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1109/led.2017.2782752

UR - https://doi.org/10.1109/led.2017.2782752

TI - An Artificial Neuron Based on a Threshold Switching Memristor

T2 - IEEE Electron Device Letters

AU - Zhang, Xumeng

AU - Wang, Wei

AU - Liu, Qi

AU - Xiaolong, Zhao

AU - Wei, Jinsong

AU - Cao, Rongrong

AU - Yao, Zhihong

AU - Zhu, Xiaoli

AU - Zhang, Feng

AU - Lv, Hangbing

AU - Long, Shibing

AU - Luo, Qing

PY - 2018

DA - 2018/02/01

PB - Institute of Electrical and Electronics Engineers (IEEE)

SP - 308-311

IS - 2

VL - 39

SN - 0741-3106

SN - 1558-0563

ER -

BibTex |

Cite this

BibTex (up to 50 authors) Copy

@article{2018_Zhang,

author = {Xumeng Zhang and Wei Wang and Qi Liu and Zhao Xiaolong and Jinsong Wei and Rongrong Cao and Zhihong Yao and Xiaoli Zhu and Feng Zhang and Hangbing Lv and Shibing Long and Qing Luo},

title = {An Artificial Neuron Based on a Threshold Switching Memristor},

journal = {IEEE Electron Device Letters},

year = {2018},

volume = {39},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

month = {feb},

url = {https://doi.org/10.1109/led.2017.2782752},

number = {2},

pages = {308--311},

doi = {10.1109/led.2017.2782752}

}

MLA

Cite this

MLA Copy

Zhang, Xumeng, et al. “An Artificial Neuron Based on a Threshold Switching Memristor.” IEEE Electron Device Letters, vol. 39, no. 2, Feb. 2018, pp. 308-311. https://doi.org/10.1109/led.2017.2782752.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Electron Device Letters

scimago Q1

wos Q2

SJR

1.150

CiteScore

7.6

Impact factor

4.5

ISSN

07413106 (Print)

15580563 (Electronic)