SPICE modeling of memristors with multilevel resistance states

doi:10.1088/1674-1056/21/9/098901

Abstract With CMOS technologies approaching the scaling ceiling, novel memory technologies have thrived in recent years, among which the memristor is a rather promising candidate for future resistive memory (RRAM). Memristor's potential to store multiple bits of information as different resistance levels allows its application in multilevel cell (MCL) technology, which can significantly increase the memory capacity. However, most existing memristor models are built for binary or continuous memristance switching. In this paper, we propose the simulation program with integrated circuits emphasis (SPICE) modeling of charge-controlled and flux-controlled memristors with multilevel resistance states based on the memristance versus state map. In our model, the memristance switches abruptly between neighboring resistance states. The proposed model allows users to easily set the number of the resistance levels as parameters, and provides the predicability of resistance switching time if the input current/voltage waveform is given. The functionality of our models has been validated in HSPICE. The models can be used in multilevel RRAM modeling as well as in artificial neural network simulations.

Keywords: memristor multilevel cell SPICE model

Received: 30 December 2011
Revised: 08 March 2012
Accepted manuscript online:

PACS:	89.20.Ff	(Computer science and technology)
	85.40.Bh	(Computer-aided design of microcircuits; layout and modeling)
	85.35.-p	(Nanoelectronic devices)
	84.32.-y	(Passive circuit components)

Fund: Project supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 60921062) and the National Natural Science Foundation of China (Grant No. 61003075).

Corresponding Authors: Fang Xu-Dong
E-mail: fangxudong850403@gmail.com

Cite this article:

Fang Xu-Dong (方旭东), Tang Yu-Hua (唐玉华), Wu Jun-Jie (吴俊杰) SPICE modeling of memristors with multilevel resistance states 2012 Chin. Phys. B 21 098901

[1]	Chung A, Deen J, Lee J S and Meyyappan M 2010 Nanotechnology 21 41
[2]	Strukov D B, Snider G S, Stewart D R and Williams R S 2008 Nature 453 80
[3]	Chua L O 1971 IEEE T. Circuit I 18 507
[4]	Yang Y C, Pan F, Liu Q, Liu M and Zeng F 2009 Nano Lett. 9 1636
[5]	Xing Z W 2011 Chin. Phys. B 20 097703
[6]	Li Y T, Long S B, Lu H B, Liu Q, Wang Q, Wang Y, Zhang S, Lian W T, Liu S and Liu L 2011 Chin. Phys. B 20 017305
[7]	Chua L O and Kang S M 1976 Proc. IEEE 64 209
[8]	Bauer M, Alexis R, Atwood G, Baltar B, Fazio A, Frary K, Hensel M, Ishac M, Javanifard J, Landgraf M, Leak D, Loe K, Mills D, Ruby P, Rozman R, Sweha S, Talreja S and Wojciechowski K 2000 IEEE International Symposium on Multiple-Valued Logic, Feb 1-6, 1995 Washington D.C., USA, p. 367
[9]	Greene K (2011-12-11) http://www.technologyreview.com/Infotech/20148
[10]	Kim H, Sah M P, Yang C J and Chua L O 2010 International Workshop on Cellular Nanoscale Networks and their Applications, Feb 2-6, 2010, Berkeley, USA, p. 1
[11]	Merkel C E, Nagpal N, Mandalapu S and Kudithipudi D 2011 International Joint Conference on Neural Networks, July 1-5, 2011, San Jose, USA, p. 3042
[12]	Yang Y C, Chen C, Zeng F and Pan F 2010 J. Appl. Phys. 107 093701
[13]	Wang S Y, Huang C W, Lee D Y, Tseng T Y and Chang T C 2010 J. Appl. Phys. 108 114110
[14]	Chang Y F, Chang T C and Chang C Y 2011 J. Appl. Phys. 110 053703
[15]	Wu M C, Lin Y W, Jang, W Y, Lin C H and Tseng T Y 2011 IEEE Electr. Device Lett. 32 1026
[16]	Lu W, Kim K H, Gaba S, Wheeler D, Cruz-Albrecht J, Hussain T and Srinivasa N 2011 Nano Lett. 12 389
[17]	Benderli S and Wey T A 2009 Electron. Lett. 45 377
[18]	Biolek Z, Biolek D and Biolková V 2009 Radioengineering 18 210
[19]	Rák A and Cserey G 2010 IEEE T. Comput. Aid. D 29 632
[20]	Lehtonen E and Laiho M 2010 International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA), Feb 3-5, 2010 Berkeley, USA, p. 1
[21]	Batas D and Fiedler H 2011 IEEE T. Nanotechnol. 10 250
[22]	Zhou J and Huang D 2012 Chin. Phys. B 21 048401
[23]	Abdalla H and Pickett M D 2011 IEEE International Symposium on Circuits and Systems (ISCAS), May 15-18, 2011 Rio de Janeiro, Brazil, p. 1832
[24]	Prodromakis T, Boon P P, Papavassiliou C and Toumazou C 2011 IEEE T. Electron. Dev. 58 3099
[25]	Zhang X, Zhou Y Z, Bi Q, Yang X H and Zu Y X 2010 Acta Phys. Sin. 59 6673 (in Chinese)
[26]	Yakopcic C, Taha T M, Subramanyam G, Pino R E and Rogers S 2011 IEEE Electr. Device Lett. 32 1436
[27]	Biolková V, Kolka Z, Biolek Z and Biolek D 2010 European Conference of Circuits Technology and Devices (ECCTD), Nov 30-Dec 2, 2010, Tenerife, Spain, p. 261
[28]	Shin S, Kim K and Kang S M 2010 IEEE T. Comput. Aid. D 29 590
[29]	Rozenberg M J, Inoue I H and Sánchez M J 2004 Phys. Rev. Lett. 92 178302
[30]	Lee J G, Kim D H, Lee J G, Kim D M and Min K S 2007 IEICE Electron. Expr. 4 600
[31]	Chua L O 2008 Int. J. Bifurcat. Chaos 18 3183
[32]	Chua L O 2011 Appl. Phys. A Mater. 102 765
[33]	Snider G S 2007 Nanotechnology 18 365202
[34]	Jo S H, Kim K H and Lu W 2009 Nano Lett. 9 496
[35]	Kim K M, Jeong D S and Hwang C S 2011 Nanotechnology 22 254002

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SPICE modeling of memristors with multilevel resistance states

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