Improved TiO2 TEAM Model Using a New Window Function

Abstract

This paper presents an extension of the threshold adaptive memristor (TEAM) model, which is derived based on the analysis of the physical tunnel barrier memristor (TBM) model. A novel window function is proposed in order to ensure the effective resolution of the boundary conditions, full scalability, and accurate imitation of the nonlinear dependence on the state dynamics of the TEAM model. A comparison with some existing nonlinear window functions is described. The achieved validation results of the enhanced TiO2 TEAM model show an improved simulation runtime by 25.3% and maintain a good prediction accuracy with a mean error of 0.1%.