Computationally efficient memristor model based on Hann window function

Abstract

Memristor is a fourth fundamental passive electric circuit element. A great deal of effort is devoted to the mathematical modeling of a memristor to figure out its complex and dynamic behavior. This research aims to address the demerits of existing mathematical models of the memristor such as computational inefficiency and inaccuracy. A novel model of the memristor with a Hann window function is developed for a Pt–TiO2–Pt based memristive devices. The threshold values for voltage, frequency and device length are identified. Unlike the existing models, the proposed model has the capability to work with a frequency between 0.5 to 100 Hz. The threshold for a voltage range varies between 0.02 to 3 V. The memristance was observed only if a device length of the proposed model varies between 5 to 250 nm. It is inferred that a memory window skews with an increase in applied voltage frequency and device length. Whereas memory window increases with the increase in applied voltage. The scaling parameter (j) is incorporated in the proposed model to make it more scalable and flexible. Results depict that the proposed model simulation runtime and elapsed time improved by 36% and 33% respectively. The accuracy of the presented model is 1.748 in terms of relative root mean square error. The proposed model exhibits low complexity with sufficient accuracy in simple, intuitive and closed-form.