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Memristor variability and stochastic physical properties modeling from a multivariate time series approach

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  • Alonso, F.J.
  • Maldonado, D.
  • Aguilera, A.M.
  • Roldán, J.B.

Abstract

A powerful time series analysis modeling technique is presented to describe cycle-to-cycle variability in memristors. These devices show variability linked to the inherent stochasticity of device operation and it needs to be accurately modeled to build compact models for circuit simulation and design purposes. A new multivariate approach is proposed for the reset and set voltages that accurately describes the statistical data structure of a resistive switching series. Experimental data were measured from advanced hafnium oxide based devices. The models reproduce the experiments correctly and a comparison of the multivariate and univariate approaches is shown for comparison.

Suggested Citation

  • Alonso, F.J. & Maldonado, D. & Aguilera, A.M. & Roldán, J.B., 2021. "Memristor variability and stochastic physical properties modeling from a multivariate time series approach," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920308535
    DOI: 10.1016/j.chaos.2020.110461
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    References listed on IDEAS

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    1. Fabien Alibart & Elham Zamanidoost & Dmitri B. Strukov, 2013. "Pattern classification by memristive crossbar circuits using ex situ and in situ training," Nature Communications, Nature, vol. 4(1), pages 1-7, October.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Aràndiga, F. & Barrera, D. & Eddargani, S. & Ibáñez, M.J. & Roldán, J.B., 2024. "Non-uniform WENO-based quasi-interpolating splines from the Bernstein–Bézier representation and applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 158-170.
    2. Maldonado, D. & Aguilera-Pedregosa, C. & Vinuesa, G. & García, H. & Dueñas, S. & Castán, H. & Aldana, S. & González, M.B. & Moreno, E. & Jiménez-Molinos, F. & Campabadal, F. & Roldán, J.B., 2022. "An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Acal, C. & Aguilera, A.M. & Alonso, F.J. & Ruiz-Castro, J.E. & Roldán, J.B., 2024. "Different PCA approaches for vector functional time series with applications to resistive switching processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 288-298.
    4. Koryazhkina, M.N. & Filatov, D.O. & Shishmakova, V.A. & Shenina, M.E. & Belov, A.I. & Antonov, I.N. & Kotomina, V.E. & Mikhaylov, A.N. & Gorshkov, O.N. & Agudov, N.V. & Guarcello, C. & Carollo, A. & S, 2022. "Resistive state relaxation time in ZrO2(Y)-based memristive devices under the influence of external noise," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Juan E. Ruiz-Castro & Christian Acal & Ana M. Aguilera & Juan B. Roldán, 2021. "A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories," Mathematics, MDPI, vol. 9(4), pages 1-16, February.

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