两类纳米级非线性忆阻器模型及串并联研究

董哲康; 段书凯; 胡小方; 王丽丹

doi:10.7498/aps.63.128502

摘要

忆阻器是一种新型的非线性动态可变电阻器，其阻值的变化依赖于通过它的电荷量或磁通量. 作为第四种基本电路元器件，忆阻器在非易失性存储器、非线性电路及系统、神经形态系统等领域中有巨大的应用潜能. 忆阻器串并联组合电路具有比单个忆阻器更为丰富的器件特性，引起了研究者越来越多的关注. 本文推导了带有窗函数的闭合形式的电荷及磁通量控制的忆阻器非线性模型，能够有效地模拟忆阻器边缘附近的非线性离子迁移现象，同时保证忆阻器的边界条件. 进一步，分别从忆阻器的器件参数和激励阈值两个角度，对忆阻器串并联电路进行了全面的理论推导和数值分析. 为了更加直观地观察忆阻器串并联特性，设计了一种基于Matlab的忆阻器串并联图形用户界面，能够清晰地展示两种分类方式下忆阻系统的器件特性，可为忆阻器组合电路的后续研究提供良好的理论参考和实验依据.

关键词:

Abstract

The memristor is a novel kind of electronic device with dynamic variable resistance that is dependent on the past history of the input current or voltage. As the fourth fundamental circuit element, the memristor captures a number of unique properties that have been found to possess attractive potentials in some promising fields such as nonvolatile memory, nonlinear circuit and system, and neuromorphic system. Additionally, compared with a circuit of single memristor, series-parallel circuit of memristors possesses more abundant device characteristics which arouses increasingly extensive interest from numerous researchers. In this paper, the mathematical closed-form charge-governed and flux-governed HP memristor nonlinear models are presented with constructive procedures. In particular, these models are more realistic by taking into account the nonlinear dopant drift effect nearby the terminals and the boundary conditions, and by adding a simple and effective window function. Furthermore, based on the internal parameters and threshold of the memristor respectively, the theoretical derivation and numerical analysis of the memristor-based series-parallel connection circuits have been made comprehensively. For obtaining the characteristics of the memristor-based combinational circuits intuitively, a graphical user interface is designed based on Matlab software, which is beneficial to displaying the properties of the memristive system clearly. The results in the present paper may provide theoretical reference and reliable experimental basis for the further development of the memristor-based combinational circuits.

Keywords:

作者及机构信息

1.
西南大学电子信息工程学院, 重庆 400715;

2.
香港城市大学机械与生物医学工程系, 香港

基金项目: 教育部新世纪优秀人才支持计划（批准号：教技函 [2013] 47号）、国家自然科学基金（批准号：61372139，61101233，60972155）、教育部“春晖计划”科研项目（批准号：z2011148）、留学人员科技活动项目（批准号：渝人社办 [2012] 186号）、重庆市高等学校优秀人才支持计划（批准号：渝教人 [2011] 65号）、重庆市高等学校青年骨干教师资助计划（批准号：渝教人[2011]65号）和中央高校基本科研业务费（批准号：XDJK2014A009，XDJK2013B011）资助的课题.

Authors and contacts

1.
School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;

2.
Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China

Funds: Project supported by the Program for New Century Excellent Talents in University of Ministry of Education of China (Grant No. [2013]47), the National Natural Science Foundation of China (Grant Nos. 61372139, 61101233, 60972155), the "Spring Sunshine Plan" Research Project of Ministry of Education of China (Grant No. z2011148), the Technology Foundation for Selected Overseas Chinese Scholars, Ministry of Personnel in China (Grant No. [2012]186), the University Excellent Talents Supporting Foundation of Chongqing, China (Grant No. [2011]65), the University Key Teacher Supporting Foundation of Chongqing, China (Grant No. [2011]65), and the Fundamental Research Fund for the Central Universities, China (Grant Nos. XDJK2014A009, XDJK2013B011).

参考文献

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[12]	Vontobel P O, Robinett W, Kuekes P J, Stewart D R, Williams R S, Straznicky J 2009 Nanotechnology 20 21
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[15]	McDonald N R, Pino R E, Rozwood P J, Wysocki B T 2010 The 2010 International Joint Conference on Neural Networks Barcelona, Spain, July 18-23, 2010 p1
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施引文献

[1]	Chua L O 1971 IEEE Trans. Circ. Syst. I 18 507
[2]	Tour J M, Tao H 2008 Nature 453 42
[3]	Strukov D B, Snider G S, Stewart D R, Williams R S 2008 Nature 453 80
[4]	Williams R S 2008 IEEE Spectr. 45 28
[5]	Hu X F, Duan S K, Wang L D, Liao X F 2011 Sci. China Inf. Sci. 41 500
[6]	Li Y T, Long S B, Lu H B, Liu Q, Wang Q, Wang Y, Zhang S, Lian W T, Liu S, Liu L 2011 Chin. Phys. B 20 017305
[7]	Itoh M, Chua L O 2008 Int. J. Bifurcat. Chaos 18 3183
[8]	Duan S K, Hu X F, Wang L D, Li C D, Mazumder P 2012 Sci. China Inf. Sci. 42 754
[9]	Bao B C, Liu Z, Xu J P 2010 Chin. Phys. B 19 030510
[10]	Hu X F, Duan S K, Wang L D, Li C D 2011 J. Univ. Electron. Technol. China 40 642
[11]	Gao S Y, Duan S K, Wang L D 2012 Adv. Mater. Res. 9 204
[12]	Vontobel P O, Robinett W, Kuekes P J, Stewart D R, Williams R S, Straznicky J 2009 Nanotechnology 20 21
[13]	Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P, Lu W 2010 Nano Lett. 10 1297
[14]	Borghetti J, Snider G S, Kuekes P J, Yang J J, Stewart D R, Williams R S 2010 Nature 464 873
[15]	McDonald N R, Pino R E, Rozwood P J, Wysocki B T 2010 The 2010 International Joint Conference on Neural Networks Barcelona, Spain, July 18-23, 2010 p1
[16]	Wang X Y, Andrew L F, Herbert H C I, Victor S, Qi W G 2012 Chin. Phys. B 21 108501
[17]	Kvatinsky S, Friedman E G, Kolodny A, Weiser U C 2012 IEEE Trans. Circ. Syst. I 60 211
[18]	Mahvash M, Parker A C 2010 IEEE International Midwest Symposium on Circuits and Systems Seattle, USA, August 1-4, 2010 p989
[19]	Fang X D, Tang Y H, Wu J J 2012 Chin. Phys. B 21 098901
[20]	Batas D, Fiedler H 2011 IEEE Trans. Nanotechnol. 2 250
[21]	Wang L D, Drakakis E, Duan S K, He P F 2012 Int. J. Bifurcat. Chaos 22 1250205
[22]	Wang L D, Duan S K 2012 Abstr. Appl. Anal. 2012 726927
[23]	Yin W H, Wang L D, Duan S K 2013 Appl. Mech. Mater. 284 2485
[24]	Biolek Z, Biolek D, Biolková V 2009 Radio. Eng. 18 210
[25]	Kim H, Sah M, Yang C, Cho S, Chua L O 2012 IEEE Trans. Circ. Syst. 59 2422
[26]	Adhikari S P, Yang C, Kim H, Chua L O 2012 IEEE Trans. Neural Netw. Learning Syst. 23 1426
[27]	Kim H, Sah M, Yang C, Roska T, Chua L O 2012 Proc. IEEE 100 2061
[28]	Prodromakis T, Peh B P, Papavassiliou C, Toumazou C 2011 IEEE Trans. Electron. Dev. 58 3099

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