A novel linear memristor model for data storage and synaptic applications

Abstract

A memristor is a passive electrical component that connects an electric charge and the magnetic flux linkage. Due to its unique features, much research has been done on the prospects of its use in the field, including neuromorphic computing systems and memory technologies, among others. In this article, we discussed the memristive model and its modelling equations and explored the current-voltage relationships. Subsequently, we elucidated the necessity of a window function and the challenges associated with previously reported window functions. Finally, we have proposed a novel window function in this article, highlighting its advantages over numerous existing ones. The proposed window function effectively resolves the boundary lock issue, boundary effect issue, limited flexibility issue and distorted pinched hysteresis issue. Using this window function, the synaptic learning capabilities of a memristive system have also been demonstrated. The flexibility offered by this window function with just two control parameters is considerable.