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 nonlinearity of the switching kinetics, and the feasibility of predicting the behavior of two antiserially connected devices correctly. We analyzed two classes of models: the first class comprises common linear memristor models and the second class widely used nonlinear memristive models. The linear memristor models are based on Strukov's initial memristor model extended by different window functions, while the nonlinear models include Pickett's 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.