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

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

Chaos, Solitons & Fractals, 2021, vol. 143, issue C

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.

Keywords: Memristors; Variability; Resistive switching memory; Conductive filaments; Time series modeling; Compact modeling; Autocovariance (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920308535

DOI: 10.1016/j.chaos.2020.110461

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