TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach.

T D Dongale,S M Kumbhar,R S Vhatkar,P J Patil,P B Patil,P K Gaikwad,P P Chougule,N K Desai,M V Takale,R K Kamat,P P Waifalkar

doi:10.1186/s40580-016-0076-8

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.

Highlights

Memristor which is poised to establish as the fourth circuit element in addition to the R, L and C, was theorized way back in the year 1971 by Chua [1]
The passivity and nonlinearity are some of the important characteristics of the memristor, which leads to its usage in the applications of diversified domains such as biomedical, resistive random access memory (RRAM), neural computing, nonlinear dynamics, neuromorphic computing realm
From the results it is clearly evident that memristor will be work as a promising RRAM building block at the higher values of control parameter, when it is modeled with piecewise linear window function

Summary

Background

Memristor which is poised to establish as the fourth circuit element in addition to the R, L and C, was theorized way back in the year 1971 by Chua [1]. Li et al reported a new modelling method with multinomial window function This method is derived through the statistical fitting of an experimental data of a memristor device [11]. Our research group too has reported two new window functions for the modeling of the memristor device [18]. While our previous papers report more of the details related to mathematical aspects, the present paper is a value addition as it showcases the simulation in light of the RRAM and neuromorphic applications. Both these applications are currently in profound demand.

Overview of piecewise linear and nonlinear window functions

Conclusion