Memristor Device Modeling

Chapter
Part of the Analog Circuits and Signal Processing book series (ACSP)

Abstract

This chapter presents a physics-based mathematical model for anionic memristor devices. The model utilizes Poisson Boltzmann equation to account for temperature effect on device potential at equilibrium and comprehends material effect on device behaviors. A detailed MATLAB-based algorithm is developed to clarify and simplify the simulation environment. Moreover, the provided model is used to simulate and predict the effect of oxide thickness, material type, and operating temperatures on the electrical characteristics of the device. The value of this contribution is to provide a framework intended to simulate anionic memristor devices using correlated mathematical models. In addition, the model can be used to explore device materials and predict its performance.

Keywords

Memristor VCM Switching Vacancy Profile Temperature Oxide Thickness Parameter Potential Voltage Poisson Boltzmann 

References

  1. 1.
    T. Prodromakis, B.P. Peh, C. Papavassiliou, C. Toumazou, A versatile memristor model with nonlinear dopant kinetics. IEEE Trans. Electron Devices 58, 3099–3105 (2011)CrossRefGoogle Scholar
  2. 2.
    S. Kvatinsky, E.G. Friedman, A. Kolodny, U.C. Weiser, TEAM: ThrEshold Adaptive Memristor Model. IEEE Trans. Circ. Syst. I-Regular Papers 60, 211–221 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    J.X. Zha, H. Huang, Y.J. Liu, A novel window function for memristor model with application in programming analog circuits. IEEE Trans. Circ. Syst. II-Express Briefs 63, 423–427 (2016)Google Scholar
  4. 4.
    S. Kvatinsky, M. Ramadan, E.G. Friedman, A. Kolodny, VTEAM: A general model for voltage-controlled memristors. IEEE Trans. Circ. Syst. II: Express Briefs 62, 786–790 (2015)Google Scholar
  5. 5.
    M.P. Sah, C. Yang, H. Kim, B. Muthuswamy, J. Jevtic, L. Chua, A generic model of memristors with parasitic components. IEEE Trans. Circ. Syst. I: Regular Papers 62, 891–898 (2015)MathSciNetGoogle Scholar
  6. 6.
    C. Yakopcic, T.M. Taha, G. Subramanyam, R.E. Pino, S. Rogers, A memristor device model. IEEE Electron Device Lett. 32, 1436–1438 (2011)CrossRefGoogle Scholar
  7. 7.
    M. Prezioso, F. Merrikh-Bayat, B.D. Hoskins, G.C. Adam, K.K. Likharev, D.B. Strukov, Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015)CrossRefGoogle Scholar
  8. 8.
    D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453, 80–83 (2008)CrossRefGoogle Scholar
  9. 9.
    C. O’Kelly, J.A. Fairfield, J.J. Boland, A single nanoscale junction with programmable multilevel memory. ACS Nano. 8, 11724–11729 (2014)Google Scholar
  10. 10.
    D.B. Strukov, J.L. Borghetti, R.S. Williams, Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior. Small 5, 1058–1063 (2009)CrossRefGoogle Scholar
  11. 11.
    N. Hashem, S. Das, Switching-time analysis of binary-oxide memristors via a nonlinear model. Appl. Phys. Lett. 100, 262106 (2012)CrossRefGoogle Scholar
  12. 12.
    Y. Taur, T.H. Ning, Fundamentals of Modern VLSI Devices (Cambridge University Press, Cambridge, 2013)Google Scholar
  13. 13.
    M. Noman, W.K. Jiang, P.A. Salvador, M. Skowronski, J.A. Bain, Computational investigations into the operating window for memristive devices based on homogeneous ionic motion. Appl. Phys. A Mater. Sci. Process. 102, 877–883 (2011)CrossRefGoogle Scholar
  14. 14.
    C. Galup-Montoro, M.C. Schneider, MOSFET Modeling for Circuit Analysis and Design (World scientific, Singapore, 2007)Google Scholar
  15. 15.
    E. Shivanian, S. Abbasbandy, M.S. Alhuthali, Exact analytical solution to the Poisson-Boltzmann equation for semiconductor devices. Eur. Phys. J. Plus 129, 104 (2014)CrossRefGoogle Scholar
  16. 16.
    A. Tangena, J. Middelhoek, N. De Rooij, Influence of positive ions on the current-voltage characteristics of MOS structures. J. Appl. Phys. 49, 2876–2879 (1978)CrossRefGoogle Scholar
  17. 17.
    H. Abunahla, D. Homouz, Y. Halawani, B. Mohammad, Modeling and device parameter design to improve reset time in binary-oxide memristors. Appl. Phys. A 117(3), 1019–1023 (2014)CrossRefGoogle Scholar
  18. 18.
    S. Kim, H.Y. Jeong, S.K. Kim, S.Y. Choi, K.J. Lee, Flexible memristive memory array on plastic substrates. Nano Lett. 11, 5438–5442 (2011)CrossRefGoogle Scholar
  19. 19.
    E. Gale, A. Adamatzky, B. de Lacy Costello, Fabrication and modelling of titanium dioxide memristors, in Proceedings RSC Younger Members Symposium (2012)Google Scholar
  20. 20.
    T.F. Bogart, J.S. Beasley, G. Rico, Electronic Devices and Circuits (Pearson/Prentice Hall, New Jersey, 2004)Google Scholar
  21. 21.
    B.G. Streetman, S. Banerjee, Solid State Electronic Devices, vol. 4 (Prentice Hall, New Jersey, 2000)Google Scholar
  22. 22.
    S. Kim, S.-J. Kim, K.M. Kim, S.R. Lee, M. Chang, E. Cho et al., Physical electro-thermal model of resistive switching in bi-layered resistance-change memory. Sci. Rep. 3, 1680 (2013)CrossRefGoogle Scholar
  23. 23.
    J. Qi, M. Olmedo, J.-G. Zheng, J. Liu, Multimode resistive switching in single ZnO nanoisland system. Sci. Rep. 3, 2405 (2013)CrossRefGoogle Scholar
  24. 24.
    D.B. Strukov, R.S. Williams, Exponential ionic drift: fast switching and low volatility of thin-film memristors. Appl. Phys. A Mater. Sci. Process. 94, 515–519 (2009)CrossRefGoogle Scholar
  25. 25.
    N.M. Muhammad, N. Duraisamy, K. Rahman, H.W. Dang, J. Jo, K.H. Choi, Fabrication of printed memory device having zinc-oxide active nano-layer and investigation of resistive switching. Curr. Appl. Phys. 13, 90–96 (2013)CrossRefGoogle Scholar
  26. 26.
    K. Chang, Y. Yeh, J. Lue, Measurement of the dielectric constants of zinc metallic nanoparticles at various frequencies. Measurement 45, 808–813 (2012)CrossRefGoogle Scholar
  27. 27.
    H. Kattelus, M. Ylilammi, J. Salmi, T. Ranta-Aho, E. Kanen, A. Suni, Electrical properties of tantalum based composite oxide films, in MRS Proceedings (1992), p. 51Google Scholar
  28. 28.
    H. Abunahla, B. Mohammad, D. Homouz, C.J. Okelly, Modeling valance change memristor device: Oxide thickness, material type, and temperature effects. IEEE Trans. Circ. Syst. I: Regular Papers 63(12), 2139–2148 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

Personalised recommendations