Memristor variability and stochastic physical properties modeling from a multivariate time series approach Alonso Morales, Francisco J. Maldonado Correa, David Aguilera Del Pino, Ana María Roldán Aranda, Juan Bautista Memristors Variability Resistive switching memory Conductive filaments Time series modeling Compact modeling Autocovariance 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. 2021-11-30T08:21:27Z 2021-11-30T08:21:27Z 2021-02 info:eu-repo/semantics/article F.J. Alonso, D. Maldonado, A.M. Aguilera, J.B. Roldán, Memristor variability and stochastic physical properties modeling from a multivariate time series approach, Chaos, Solitons & Fractals, Volume 143, 2021, 110461, ISSN 0960-0779, https://doi.org/10.1016/j.chaos.2020.110461 http://hdl.handle.net/10481/71825 https://doi.org/10.1016/j.chaos.2020.110461 eng http://creativecommons.org/licenses/by-nd/3.0/es/ info:eu-repo/semantics/embargoedAccess Atribución-SinDerivadas 3.0 España Elsevier