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Drift speed adaptive memristor model

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Abstract

Different memristive devices have different characteristic curves; how to describe and simulate various kinds of memristive devices with a unified model is still a significant work. In this work, a new memristor model is presented—DSAM, drift speed adaptive memristor model. This model is composed of a linear i–v relation function and a speed adaptive state function. A detailed analysis of model parameters’ effect is proposed. It is shown that different parameters perform different drift speed curves, which can be adjusted to describe various memristive devices. The proposed model can also adapt to various voltage inputs. Finally, the model is tested in fitting different memristor devices with an average error of less than \(5.5 \%\).

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The work was supported by the National Nature Science Foundation of China under Grant (Nos. 61674054, 62101142) and with the Natural Science Foundation of Hunan Province of China under Grant (No. 2017JJ2049) and with the Science and Technology Program of Guangzhou of China under Grant (No. 201904010302) and with the Guangzhou Science and Technology Plan Research Project under Grant (No. 202102020874) and with innovation Project for Higher Education of Guangdong Province under Grant (No. 2021KTSCX062) and with Key Research Platform Project for Higher Education of Guangdong Province under Grant (No. 2021ZDZX1079) and with the Fundamental Research Funds for the Central Universities (No. 531118010418).

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Authors and Affiliations

  1. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Ya Li & Lijun Xie

  2. School of Physics and Electronics, Hunan University, Changsha, 410082, China

    Pingdan Xiao

  3. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Ciyan Zheng

  4. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Qinghui Hong

  5. Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Ya Li

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  1. Ya Li
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Correspondence to Qinghui Hong.

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Li, Y., Xie, L., Xiao, P. et al. Drift speed adaptive memristor model. Neural Comput & Applic 35, 14419–14430 (2023). https://doi.org/10.1007/s00521-023-08401-7

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