Applicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices

Abstract

Highly accurate and predictive models of resistive switching devices are needed to enable future memory and logic design. Widely used is the memristive modeling approach considering resistive switches as dynamical systems. Here we introduce three evaluation criteria for memristor models, checking for plausibility of the I-V characteristics, the presence of a sufficiently non-linearity of the switching kinetics, and the feasibility of predicting the behavior of two anti-serially connected devices correctly. We analyzed two classes of models: the first class comprises common linear memristor models and the second class widely used non-linear memristive models. The linear memristor models are based on Strukovs initial memristor model extended by different window functions, while the non-linear models include Picketts physics-based memristor model and models derived thereof. This study reveals lacking predictivity of the first class of models, independent of the applied window function. Only the physics-based model is able to fulfill most of the basic evaluation criteria.

arXiv comment9 pages; accepted for IEEE TCAS-I

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Cited by in corpus (6)

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• A Complementary Resistive Switch-based Crossbar Array Adder
• An experimental proof that resistance-switching memories are not memristors
• A physical memristor based Muthuswamy-Chua-Ginoux system
• Variability-aware Memristive Crossbars -- A Tutorial
• Subcritical Andronov-Hopf scenario for systems with a line of equilibria