Skip to main content
SpringerLink
Log in

An efficient and flexible window function for a memristor model and its analog circuit application

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

Abstract

The memristor is a novel nanostructured resistive tuning two-terminal electronic device that has been widely explored in the areas of neuromorphic computing systems, memory, digital circuits, analog circuits, and many more new applications. In this article, an efficient and flexible window function is presented for a linear drift memristor model. The proposed parametric cubic parabolic window function provides a unique feature (controllable window function discontinuity at the boundaries) to a linear drift memristor model by which the distorted pinched hysteresis loop problem is resolved and the number of programming resistance states of the memristor is improved. Five control parameters are introduced in the proposed window function in order to correct the existing problems (such as boundary effect, boundary lock and inflexibility) and are able to provide asymmetric nonlinearity at the boundaries of the device, making it feasible for tracking the resistive switching dynamic of a futuristic oxide-based memristive device with different inert electrodes. The proposed model is validated with a solution-processed ZnO-based fabricated memristive device. A programmable analog gain amplifier circuit is ultimately executed to simulate the utilization of the evolved memristor model, and the effect of memristance resolution is investigated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  1. Chua, L.: Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)

    Article  Google Scholar 

  2. Strukov, D.B., Snider, G.S., Stewart, D.R., Stanley Williams, R.: The missing memristor found. Nature 453(7191), 80–83 (2008)

    Article  Google Scholar 

  3. Yang, J.J., Pickett, M.D., Li, X., Ohlberg, D.A.A., Stewart, D.R., Stanley Williams, R.: Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3(7), 429–433 (2008)

    Article  Google Scholar 

  4. Yakopcic, C., Taha, T.M., Subramanyam, G., Pino, R.E., Rogers, S.: A memristor device model. IEEE Electron Device Lett. 32(10), 1436–1438 (2011)

    Article  Google Scholar 

  5. Pickett, M.D., Strukov, D.B., Borghetti, J.L., Joshua Yang, J., Snider, G.S., Stewart, D.R., Stanley Williams, R.: Switching dynamics in titanium dioxide memristive devices. J. Appl. Phys. 106(7), 074508 (2009)

    Article  Google Scholar 

  6. Kvatinsky, S., Friedman, E.G., Kolodny, A., Weiser, U.C.: TEAM: threshold adaptive memristor model. IEEE Trans. Circuits Syst. I Regul. Pap. 60(1), 211–221 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kvatinsky, S., Ramadan, M., Friedman, E.G., Kolodny, A.: VTEAM: a general model for voltage-controlled memristors. IEEE Trans. Circuits Syst. II Express Briefs 62(8), 786–790 (2015)

    Google Scholar 

  8. Singh, J., Raj, B.: Modeling of mean barrier height levying various image forces of metal–insulator–metal structure to enhance the performance of conductive filament based memristor model. IEEE Trans. Nanotechnol. 17(2), 268–275 (2018)

    Article  Google Scholar 

  9. Rak, A., Cserey, G.: Macromodeling of the memristor in SPICE. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 29(4), 632–636 (2010)

    Article  Google Scholar 

  10. Benderli, S., Wey, T.A.: On SPICE macromodelling of TiO 2 memristors. Electron. Lett. 45(7), 377–379 (2009)

    Article  Google Scholar 

  11. Duan, S.K., XiaoFang, H., Wang, L.D., Li, C.D., Mazumder, P.: Memristor-based RRAM with applications. Sci. China Inf. Sci. 55(6), 1446–1460 (2012)

    Article  Google Scholar 

  12. Adhikari, S.P., Kim, H., Budhathoki, R.K., Yang, C., Chua, L.O.: A circuit-based learning architecture for multilayer neural networks with memristor bridge synapses. IEEE Trans. Circuits Syst. I Regul. Papers 62(1), 215–223 (2014)

    Article  Google Scholar 

  13. Li, J., Dong, Z., Luo, Li., Duan, S., Wang, L.: A novel versatile window function for memristor model with application in spiking neural network. Neurocomputing 405, 239–246 (2020)

    Article  Google Scholar 

  14. Wen, S., Xie, X., Yan, Z., Huang, T., Zeng, Z.: General memristor with applications in multilayer neural networks. Neural Netw. 103, 142–149 (2018)

    Article  Google Scholar 

  15. Zha, J., Huang, He., Liu, Y.: A novel window function for memristor model with application in programming analog circuits. IEEE Trans. Circuits Syst. II Express Briefs 63(5), 423–427 (2015)

    Google Scholar 

  16. Anusudha, T.A., Prabaharan, S.R.S.: A versatile window function for linear ion drift memristor model–A new approach. AEU-Int. J. Electron. Commun. 90, 130–139 (2018)

    Article  Google Scholar 

  17. Zha, J., Huang, He., Huang, T., Cao, J., Alsaedi, A., Alsaadi, F.E.: A general memristor model and its applications in programmable analog circuits. Neurocomputing 267, 134–140 (2017)

    Article  Google Scholar 

  18. Xu, J., Wang, H., Zhu, Y., Liu, Y., Zou, Z., Li, G., Xiong, R.: Tunable digital-to-analog switching in Nb2O5-based resistance switching devices by oxygen vacancy engineering. Appl. Surf. Sci. 579, 152114 (2022)

    Article  Google Scholar 

  19. Das, M., Kumar, A., Singh, R., Htay, M.T., Mukherjee, S.: Realization of synaptic learning and memory functions in Y2O3 based memristive device fabricated by dual ion beam sputtering. Nanotechnology 29(5), 055203 (2018)

    Article  Google Scholar 

  20. Singh, C.P., Pandey, S.K.: Performance analysis of forming free switching dynamics of e-beam evaporated SnOx based resistive switching device. IEEE Trans. Electron Devices 69(5), 2686–2691 (2022)

    Article  Google Scholar 

  21. Joglekar, Y.N., Wolf, S.J.: The elusive memristor: properties of basic electrical circuits. Eur. J. Phys. 30(4), 661 (2009)

    Article  MATH  Google Scholar 

  22. Biolek, Z., Biolek, D., Biolkova, V.: SPICE model of Memristor with nonlinear dopant drift. Radioengineering 18(2), 210 (2009)

    MATH  Google Scholar 

  23. Prodromakis, T., Boon, P.P., Christos, P., Christofer, T.: A versatile memristor model with nonlinear dopant kinetics. IEEE Trans. Electron Devices 58(9), 3099–3105 (2011)

    Article  Google Scholar 

  24. Chen, W., Xiao, Y., Frank Z.W.: An omnipotent memristor model with controllable window functions. In: 2015 17th UKSim-AMSS international conference on modelling and simulation (UKSim), pp. 600–605. IEEE (2015)

  25. Mladenov, V., Kirilov, S.: A nonlinear drift memristor model with a modified biolek window function and activation threshold. Electronics 6(4), 77 (2017)

    Article  Google Scholar 

  26. Chowdhury, J., Das, J.K., Rout, N.K.: Trigonometric window functions for memristive device modeling. In: 2015 Fifth international conference on advanced computing & communication technologies, pp. 157–161. IEEE, (2015)

  27. Shi, M., Yajuan, Yu., Qi, Xu.: Window function for fractional-order HP non-linear memristor model. IET Circuits Devices Syst. 12(4), 447–452 (2018)

    Article  Google Scholar 

  28. Shin, S., Kim, K., Kang, S.-M.: Memristor applications for programmable analog ICs. IEEE Trans. Nanotechnol. 10(2), 266–274 (2010)

    Article  Google Scholar 

  29. Patil, S.R., Chougale, M.Y., Rane, T.D., Khot, S.S., Patil, A.A., Bagal, O.S., Jadhav, S.D., Sheikh, A.D., Kim, S., Dongale, T.D.: Solution-processable ZnO thin film memristive device for resistive random access memory application. Electronics 7(12), 445 (2018)

    Article  Google Scholar 

  30. Pershin, Y.V., Massimiliano-Di-Ventra: Practical approach to programmable analog circuits with memristors. IEEE Trans. Circuits Syst. I Regul. Papers 57(8), 1857–1864 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Indian Institute of Technology, Patna, India

    Chandra Prakash Singh,  Raghvendra & Saurabh Kumar Pandey

Authors
  1. Chandra Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raghvendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saurabh Kumar Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandra Prakash Singh.

Ethics declarations

Conflict of interest

The authors have not disclosed any competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, C.P., Raghvendra & Pandey, S.K. An efficient and flexible window function for a memristor model and its analog circuit application. J Comput Electron 21, 1425–1433 (2022). https://doi.org/10.1007/s10825-022-01939-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-022-01939-0

Keywords

Search

Navigation