An improved memristor model for brain-inspired computing

doi:10.1088/1674-1056/26/11/118502

Abstract Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors' performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover, the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

Keywords: memristor drift diffusion model synaptic brain-inspired computing

Received: 10 May 2017
Revised: 17 August 2017
Accepted manuscript online:

PACS:	85.35.-p	(Nanoelectronic devices)
	87.19.lv	(Learning and memory)
	87.19.lw	(Plasticity)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61332003) and High Performance Computing Laboratory, China (Grant No. 201501-02).

Corresponding Authors: Liang Fang
E-mail: lfang@nudt.edu.cn

Cite this article:

Errui Zhou(周二瑞), Liang Fang(方粮), Rulin Liu(刘汝霖), Zhenseng Tang(汤振森) An improved memristor model for brain-inspired computing 2017 Chin. Phys. B 26 118502

[1]	Chua L O 1971 IEEE Tran. Circ. Th. 18 507
[2]	Strukov D B, Snider G S, Stewart D R and Williams R S 2008 Nature 453 80
[3]	Zhou J, Yang X J, Wu J J, Zhu X, Fang X D and Huang D 2013 Sci. China:Inf. Sci. 56 1
[4]	Zhu X, Tang Y H, Wu C Q, Wu J J and Yi X 2014 Chin. Phys. B 23 028501
[5]	Su S, Jian X C, Wang F, Han Y M, Tian Y X, Wang X Y, Zhang H Z and Zhang K L 2016 Chin. Phys. B 25 107302
[6]	Li Z J and Zheng Y C 2013 Chin. Phys. B 22 040502
[7]	Huang D, Wu J J and Tang Y H 2014 Chin. Phys. B 23 038404
[8]	Jo S H, Chang T Ebong I, Bhadviya B B, Mazumder P and Lu W 2010 Nano Lett. 10 1297
[9]	Covi E, Brivio S, Serb A, Prodromakis T, Fanciulli M and Spiga S 2016 Front. Neurosci. 10 482
[10]	Zhang Y, Li Y, Wang X P and Friedman E G 2017 IEEE Tran. Elec. Dev. 64 1806
[11]	Joglekar Y N and Wolf S J 2009 Eur. J. Phys. 30 661
[12]	Biolek Z, Biolek D and Biolkova V 2009 Radioengineering 18 210
[13]	Prodromakis T, Peh B P, Papavassiliou C and Toumazou C 2011 IEEE Tran. Elec. Dev. 58 3099
[14]	Zha J X, Huang H and Liu Y J 2016 IEEE Tran. Circuits Syst. Ⅱ:Exp. Briefs 63 423
[15]	Chang T, Jo S H, Kim K H, Sheridan P, Gaba S and Lu W 2011 Appl. Phys. A 102 857
[16]	Chang T, Jo S H and Lu W 2011 ACS Nano 5 7669
[17]	Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C and Zhu X J 2012 Adv. Funct. Mater. 22 2759
[18]	Meng F Y, Duan S K, Wang L D, Hu X F and Dong Z K 2015 Acta Phys. Sin. 64 148501(in Chinese)
[19]	Shao N, Zhang S B and Shao S Y 2016 Acta Phys. Sin. 65 128503(in Chinese)
[20]	Strukov D B, Alibart F and Wiliams R S 2012 Appl. Phys. A 107 509
[21]	Berdan R, Lim C, Khiat A and Papavassiliou C 2014 IEEE Elec. Dev. Lett. 35 135
[22]	Chen L, Li C D, Huang T W, Ahmad H G and Chen Y R 2014 Phys. Lett. A 378 2924

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