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

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.

References in corpus (1)

• Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors

Cited by in corpus (5)

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• Noise-Assisted Metastability: From Lévy Flights to Memristors, Quantum Escape, and Josephson-based Axion Searches