TiO2 based Nanostructured Memristor for RRAM and Neuromorphic Applications: A Simulation Approach

TL;DR

This paper presents a simulation study of TiO2 nanostructured memristors using novel window functions, demonstrating their potential for RRAM and neuromorphic applications through improved nonlinear behavior modeling.

Contribution

The paper introduces a new scalable nonlinear window function for memristor simulation, enhancing modeling accuracy for neuromorphic and RRAM applications.

Findings

Piecewise linear window function effectively simulates RRAM characteristics.

Nonlinear window function shows nonlinear phenomena at low control parameters.

Proposed nonlinear window function exhibits smooth switching behavior suitable for neuromorphic systems.

Abstract

We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simulate the memristor characteristics pertaining to RRAM application. However, the nonlinear window function could exhibit the nonlinear phenomenon in simulation only at the lower magnitude of control parameter. This has motivated us to propose a new nonlinear window function for emulating the simulation model of the memristor. Interestingly, the proposed window function is scalable up to f(x)=1 and exhibits the nonlinear behavior at higher magnitude of control parameter. Moreover, the simulation results of proposed nonlinear window function are encouraging and reveals the smooth nonlinear change from LRS to HRS and vice versa and therefore useful for the neuromorphic applications.