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A new nonlinear dopant kinetic model of memristor and its application

  • F Xu
  • J Q Zhang
  • S F Huang
  • J S Zhang
  • S Q Xie
  • M S Wang
Original Paper
  • 23 Downloads

Abstract

To improve the effectiveness of memristor model in complex network, the drift model of memristor is further optimized. In this paper, based on both the existing nonlinear doping kinetic model and sinusoidal function, a new model is proposed, which agrees with the five criteria for describing the migration characteristics of nonlinear dopant. Secondly, to improve the flexibility of the new model, two in-built control parameters are introduced, and it is verified by using the coupled variable-resistor model proposed by HP research team. The simulation results show that the new model is well matched with the authoritative memristor model. At the same time, by constructing the Simulink model of memristor we have verified the effectiveness of the new model. Finally, the new model is applied to the three-node Hopfield neural network, and the dynamic behaviors of this network have been investigated. In particular, we have introduced an electromagnetic induction coefficient to describe the possible electromagnetic field effect caused by signal transmission in the network. The results will provide a new idea for the memristor to be widely used in complex network, artificial synapse and many other fields.

Keywords

Memristor Nonlinear dopant kinetics Window function Hopfield neural network Chaotic behaviors 

PACS Nos.

85.35.-p 05.45.-a 87.19.lj 

Notes

Acknowledgements

The project supported by the Natural Science Foundation of Anhui Province, China (No. 1508085MA15), the Key project of cultivation of leading talents in Universities of Anhui Provence (No. gxbjZD2016014), the Innovation and practice research project of graduate students of Anhui Normal University, China (No. 2017cxsj045), the project of Academic and technical leaders candidate of Anhui Province (2017H117), and the Natural Science Foundation of the Anhui Higher Education Institutions (No. KJ2017A331).

References

  1. [1]
    L O Chua IEEE Trans. Circuit Theory. 18 507 (1971)CrossRefGoogle Scholar
  2. [2]
    M Itoh and L O Chua Int. J. Bifurc. Chaos. 18 3183 (2008)CrossRefGoogle Scholar
  3. [3]
    B C Bao, F W Hu, Z Liu and J Z Xu Chin. Phys. B. 23 303 (2014)Google Scholar
  4. [4]
    B C Bao, P Jiang, H G Wu and F W Hu Nonlinear Dyn. 79 2333 (2015)CrossRefGoogle Scholar
  5. [5]
    S H Jo, T Chang, I Ebong, B Bhadviya, P Mazumder, and W Lu Nano Lett. 10 1297 (2010)ADSCrossRefGoogle Scholar
  6. [6]
    J P Carbajal, J Dambre, M Hermans, and B Schrauwen Neural Comput. 27 1 (2014)Google Scholar
  7. [7]
    S N Truong, K V Pham, W Yang, Kyeong-Sik Min, Y Abbas, C J Kang, S Shin and K Pedrotti J. Korean Phys. Soc. 69 640 (2016)Google Scholar
  8. [8]
    D B Strukov, G S Snider, D R Stewart and R S Williams Nature 453 80 (2008)Google Scholar
  9. [9]
    Y J Joshua, F Miao, MD Pickett, DA Ohlberg, DR Stewart, CN Lau and RS Williams Nanotechnology 20 1 (2009)Google Scholar
  10. [10]
    A Lancichinetti, M Kivelä, J Saramäki and Fortunato S, Plos One 5 e11976 (2010)ADSCrossRefGoogle Scholar
  11. [11]
    F M Bayat and S B Shouraki, Neural Comput. Appl. 26 67 (2015)CrossRefGoogle Scholar
  12. [12]
    F Xu, J Q Zhang, T T fang, S F Huang and M S Wang Nonlinear Dyn. 92 1395 (2018)CrossRefGoogle Scholar
  13. [13]
    W Zhang, C Li, T Huang and X He IEEE T Neural Netw. Learn. 26 3308 (2015)ADSGoogle Scholar
  14. [14]
    T Prodromakis, B P Peh, C Papavassiliou and C Toumazou IEEE Trans. Electron Dev. 58 3099 (2011)ADSCrossRefGoogle Scholar
  15. [15]
    Y N Joglekar and S J Wolf Eur. J Phys. 30 661 (2009)CrossRefGoogle Scholar
  16. [16]
    Z Biolek, D Biolek and V Biolkova Radioengineering 18 210 (2009)Google Scholar
  17. [17]
    P Bansal and B Raj J. Comput. Theor. Nanosci. 14 2319 (2017)CrossRefGoogle Scholar
  18. [18]
    J Yu, X Mu, X Xi and S. Wang Radioengineering 22, 969 (2013)Google Scholar
  19. [19]
    J Zha, H Huang and Y. Liu IEEE Trans. Circuits-II 63 423 (2016)Google Scholar
  20. [20]
    T. D. Dongale, P. J. Patil, N. K. Desai,P. P. Chougule, S. M. Kumbhar, P. P. Waifalkar, P. B. Patil,R. S. Vhatkar, M. V. Takale, P. K. Gaikwad and R. K. Kamat Nano Converg. 3 16 (2016)Google Scholar
  21. [21]
    E. Gale, arXiv:1106.3170v1 (cond-mat.mtrl-sci), unpublishedGoogle Scholar
  22. [22]
    L S Liang, J Q Zhang, L Z Liu, M S Wang, B H Wang Chin. Phys. Lett. 31 050502 (2014)ADSCrossRefGoogle Scholar
  23. [23]
    B C Bao, H Qian, Q Xu, M Chen, J Wang and Y J Yu Front. Comput. Neurosc. 11 1 (2017)Google Scholar
  24. [24]
    B C Bao, H Qian, J Wang, Q Xu, M Chen, H G Wu and Y J Yu Nonlinear Dyn. 10 1 (2017)Google Scholar
  25. [25]
    X S Yang and Y Huang Chaos 16 033114 (2006)ADSCrossRefGoogle Scholar
  26. [26]
    Q Li, X S Yang and F Yang Neurocomputing 67 275 (2005)Google Scholar
  27. [27]
    Q Li, S Tang, H Zeng and T Zhou Nonlinear Dyn. 78 1087 (2014)CrossRefGoogle Scholar
  28. [28]
    J Ma and J Tang Sci. China Technol. Sc. 58 2038 (2015)CrossRefGoogle Scholar
  29. [29]
    M Lv, C Wang, G Ren, J Ma and X Song Nonlinear Dyn. 85 1479 (2016)CrossRefGoogle Scholar
  30. [30]
    F Xu, J Q Zhang, M Jin, S F Huang and T T Fang Nonlinear Dyn. (2018).  https://doi.org/10.1007/s11071-018-4393-9 CrossRefGoogle Scholar
  31. [31]
    J Ma and J Tang Nonlinear Dyn. 20 1 (2017)Google Scholar
  32. [32]
    Y Wang, J Ma, Y Xu, F Wu and P Zhou Int. J Bifurc. Chaos 27 1750030 (2017)CrossRefGoogle Scholar

Copyright information

© Indian Association for the Cultivation of Science 2018

Authors and Affiliations

  1. 1.College of Physics and Electronic InformationAnhui Normal UniversityWuhuPeople’s Republic of China

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