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Journal of Computational Electronics

, Volume 18, Issue 2, pp 640–647 | Cite as

An accurate and generic window function for nonlinear memristor models

  • Jeetendra SinghEmail author
  • Balwinder Raj
Article
  • 168 Downloads

Abstract

Memristors have become promising candidates for the advancement of recent technology as the miniaturization of complementary metal–oxide–semiconductor (CMOS) technology approaches its final stage. Nanoscale size, easy fabrication, compatibility with MOS, and diverse applications have accelerated these devices to new levels. In this paper, we discuss the merits and demerits of existing window functions and propose a novel window function that addresses their limitations. The suggested window function exhibits high nonlinearity at the boundaries and resolves other boundary issues. The results obtained using the proposed window function are compared with data reported in the literature to validate our design approach.

Keywords

Memristor Nonlinear drift Window function 

Notes

References

  1. 1.
    Chua, L.O.: Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)CrossRefGoogle Scholar
  2. 2.
    Widrow, B.: An Adaptive “ADALINE” Neuron Using Chemical “Memistors”. Stanford Electronics Laboratories Technical Report, No. 1553-2 (1960)Google Scholar
  3. 3.
    Chua, L.O., Kang, S.M.: Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(7191), 80–83 (2008)CrossRefGoogle Scholar
  5. 5.
    Yang, J.J., Pickett, M.D., Li, X., Ohlberg, D.A.A., Stewart, D.R., Williams, R.S.: Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3(7), 429–433 (2008)CrossRefGoogle Scholar
  6. 6.
    Lehtonen, E., Laiho, M.: BCNN using memristors for neighborhood connections. In: Proceedings of 12th International Workshop on Cellular Nanoscale Networks and Their Applications (2010)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Pickett, M.D., Strukov, D.B., Borghetti, J.L., Yang, J.J., Snider, G.S., Stewart, D.R., Williams, R.S.: Switching dynamics in titanium dioxide memristive devices. J. Appl. Phys. 106(7), 1–6 (2009)CrossRefGoogle Scholar
  9. 9.
    Abdalla, H., Pickett, M.D.: SPICE modeling of memristors. In: 2011 IEEE International Symposium of Circuits and Systems (ISCAS) (2011)Google Scholar
  10. 10.
    Biolek, Z., Biolek, D., Biolkova, V.: Spice model of memristor with nonlinear dopant drift. Radioengineering 18(2), 210–214 (2009)zbMATHGoogle Scholar
  11. 11.
    Biolek, D., Di Ventra, M., Pershin, Y.V.: Reliable SPICE simulations of memristors, memcapacitors and meminductors. Radioengineering 22(4), 945–968 (2013)Google Scholar
  12. 12.
    Vourkas, I., Batsos, A., ChSirakoulis, G.: SPICE modeling of nonlinear memristive behavior. Int. J. Circuit Theory Appl. 43(5), 553–565 (2015)CrossRefGoogle Scholar
  13. 13.
    Pershin, Y.V., Di Ventra, M.: SPICE model of memristive devices with threshold. Radioengineering 22(2), 485–489 (2013)Google Scholar
  14. 14.
    Benderli, S., Wey, T.A.: On SPICE macro modeling of TiO2 memristors. Electron. Lett. 45(7), 377–379 (2009)CrossRefGoogle Scholar
  15. 15.
    Rák, Á., Cserey, G.: Macromodeling of the memristor in SPICE. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 29(4), 632–636 (2010)CrossRefGoogle Scholar
  16. 16.
    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 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kvatinsky, S., Ramadan, M., Friedman, E.G., Kolodny, A.: VTEAM: a general model for voltage-controlled memristors. IEEE Trans. Circuits Syst. II Exp. Briefs 62(8), 786–790 (2015)CrossRefGoogle Scholar
  18. 18.
    Strukov, D.B., Borghetti, J.L., Williams, R.S.: Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior. Small 5(9), 1058–1063 (2009)CrossRefGoogle Scholar
  19. 19.
    Waser, R., Dittimann, R., Staikov, G., Szot, K.: Redox-based resistive switching memories-nanoionic mechanisms, prospects and challenges. Adv. Mater. 21(25–26), 2632–2663 (2009)CrossRefGoogle Scholar
  20. 20.
    Strukov, D.B., Williams, R.S.: Exponential ionic drift: fast switching and low volatility of thin-film memristors. Appl. Phys. A 94(3), 515–519 (2009)CrossRefGoogle Scholar
  21. 21.
    Joglekar, Y.N., Wolf, S.J.: The elusive memristor: properties of basic electrical circuits. Eur. J. Phys. 30(4), 661 (2009)CrossRefzbMATHGoogle Scholar
  22. 22.
    Prodromakis, T., Peh, B.P., Papavassiliou, C., Toumazou, C.: A versatile memristor model with nonlinear dopant kinetics. IEEE Trans. Electron Devices 58(9), 3099–3105 (2011)CrossRefGoogle Scholar
  23. 23.
    Zha, J., Huang, H., Liu, Y.: A novel window function for memristor model with application in programming analog circuits. IEEE Trans. Circuits Syst. II Exp. Briefs 63(5), 423–427 (2016)CrossRefGoogle Scholar
  24. 24.
    Blanc, J., Staebler, D.L.: Electrocoloration in SrTiO3: vacancy drift and oxidation–reduction of transition metals. Phys. Rev. B 4(10), 3548–3557 (1971)CrossRefGoogle Scholar
  25. 25.
    Alvbrant, J., Keshmiri, V., Wikner, J.J.: Transfer characteristics and bandwidth limitation in a linear-drift memristor model. In: 2015 European Conference on Circuit Theory and Design (ECCTD), pp. 1–4. IEEE (2015)Google Scholar
  26. 26.
    Butusov, D.N., Ostrovskii, V.Y., Zubarev, A.V.: Study of two-memristor circuit model with explicit composition method. In: 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2017, pp. 206–209. IEEE (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.VLSI Design Lab, Department of ECENIT JalandharJalandharIndia

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