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Chin. Phys. B, 2017, Vol. 26(11): 118501    DOI: 10.1088/1674-1056/26/11/118501
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

A phenomenological memristor model for synaptic memory and learning behaviors

Nan Shao(邵楠)1, Sheng-Bing Zhang(张盛兵)1, Shu-Yuan Shao(邵舒渊)2
1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Abstract  Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties include the forgetting effect, the transition from short-term memory (STM) to long-term memory (LTM), learning-experience behavior, etc. The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design. In our analysis of the existing memristor model with these properties, we find that some behaviors of the model are inconsistent with the reported experimental observations. A phenomenological memristor model is proposed for this kind of memristor. The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors. Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors. Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.
Keywords:  memristor model      forgetting effect      transition from short-term memory (STM) to long-term memory (LTM)      learning-experience behavior  
Received:  18 March 2017      Revised:  04 July 2017      Accepted manuscript online: 
PACS:  85.35.-p (Nanoelectronic devices)  
  73.43.Cd (Theory and modeling)  
  87.19.lv (Learning and memory)  
Corresponding Authors:  Nan Shao     E-mail:  shao@mail.nwpu.edu.cn

Cite this article: 

Nan Shao(邵楠), Sheng-Bing Zhang(张盛兵), Shu-Yuan Shao(邵舒渊) A phenomenological memristor model for synaptic memory and learning behaviors 2017 Chin. Phys. B 26 118501

[1] Chang T, Jo S H and Lu W 2011 ACS Nano 5 7669
[2] Hermiz J, Chang T, Du C and Lu W 2013 Appl. Phys. Lett. 102 083106
[3] Hu S G, Liu Y, Liu Z, Chen T P, Yu Q, Deng L J, Yin Y and Hosaka S 2014 J. Appl. Phys. 116 214502
[4] Yang R, Terabe K, Yao Y P, Tsuruoka T, Hasegawa T, Gimzewski J K and Aono M 2013 Nanotechnology 24 384003
[5] Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C and Zhu X J 2012 Adv. Funct. Mater. 22 2759
[6] Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K and Aono M 2011 Nat. Mater. 10 591
[7] Li S Z, Zeng F, Chen C, Liu H Y, Tang G S, Gao S, Song C, Lin Y S, Pan F and Guo D 2013 J. Mater. Chem. C 1 5292
[8] Lei Y, Liu Y, Xia Y D, Gao X, Xu B, Wang S D, Yin J and Liu Z G 2014 AIP Adv. 4 077105
[9] Xiao Z G and Huang J S 2016 Adv. Electron. Mater. 2 1600100
[10] Liu G, Wang C, Zhang W B, Pan L, Zhang C C, Yang X, Fan F, Chen Y and Li R W 2016 Adv. Electron. Mater. 2 1500298
[11] Zhang C C, Tai Y T, Shang J, Liu G, Wang K L, Hsu C, Yi X H, Yang X, Xue W H, Tan H W, Guo S S, Pan L and Li R W 2016 J. Mater. Chem. C 4 3217
[12] Nayak A, Ohno T, Tsuruoka T, Terabe K, Hasegawa T, Gimzewski J K and Aono M 2012 Adv. Funct. Mater. 22 3606
[13] Luo W Q, Yuan F Y, Wu H Q, Pan L Y, Deng N, Zeng F, Wei R S and Cai X J 2015 15th Non-Volatile Memory Technology Symposium (NVMTS), October 12-14, 2015, Beijing, China, 7457490
[14] Hu X F, Duan S K, Chen G R and Chen L 2017 Neurocomputing 223 129
[15] Zhang P J, Li C D, Huang T W, Chen L and Chen Y R 2017 Neurocomputing 222 47
[16] Du C, Ma W, Chang T, Sheridan P and Lu W D 2015 Adv. Funct. Mater. 25 4290
[17] Li Q J, Serb A, Prodromakis T and Xu H 2015 PLoS ONE 10 e0120506
[18] Berdan R, Lim C, Khiat A, Papavassiliou C and Prodromakis T 2014 IEEE Electron Device Lett. 35 135
[19] Chang T, Jo S H, Kim K H, Sheridan P, Gaba S and Lu W 2011 Appl. Phys. A 102 857
[20] Chen L, Li C D, Huang T W, Chen Y R, Wen S P and Qi J T 2013 Phys. Lett. A 377 3260
[21] Chen L, Li C D, Huang T W, Ahmad H G and Chen Y R 2014 Phys. Lett. A 378 2924
[22] Strukov D B, Snider G S, Stewart D R and Williams R S 2008 Nature 453 80
[23] Chen L, Li C D, Huang T W, Hu X F and Chen Y R 2016 Neurocomputing 171 1637
[24] Meng F Y, Duan S K, Wang L D, Hu X F and Dong Z K 2015 Acta Phys. Sin. 64 148501(in Chinese)
[25] Shao N, Zhang S B and Shao S Y 2016 Acta Phys. Sin. 65 128503(in Chinese)
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