Highly accurate and predictive models of resistive switching devices areneeded to enable future memory and logic design. Widely used is the memristivemodeling approach considering resistive switches as dynamical systems. Here weintroduce three evaluation criteria for memristor models, checking forplausibility of the I-V characteristics, the presence of a sufficientlynon-linearity of the switching kinetics, and the feasibility of predicting thebehavior of two anti-serially connected devices correctly. We analyzed twoclasses of models: the first class comprises common linear memristor models andthe second class widely used non-linear memristive models. The linear memristormodels are based on Strukovs initial memristor model extended by differentwindow functions, while the non-linear models include Picketts physics-basedmemristor model and models derived thereof. This study reveals lackingpredictivity of the first class of models, independent of the applied windowfunction. Only the physics-based model is able to fulfill most of the basicevaluation criteria.
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