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

机译:用于RRAM和神经形态应用的基于TiO2的纳米结构忆阻器:一种模拟方法

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摘要

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.
机译:我们报告了使用分段线性和非线性窗口函数对RRAM和神经形态应用程序进行的纳米结构忆阻器器件的仿真。已经将忆阻器的线性漂移模型用于仿真目的,以线性和非线性窗口函数为数学和脚本基础。结果表明,分段线性窗口函数可以恰当地模拟与RRAM应用有关的忆阻器特性。但是,非线性窗口函数仅在较低的控制参数幅度下才能在仿真中表现出非线性现象。这促使我们提出了一种新的非线性窗口函数,用于模拟忆阻器的仿真模型。有趣的是,拟议的窗口函数可扩展至f(x)= 1并在更高的控制参数幅度下表现出非线性行为。此外,所提出的非线性窗口函数的仿真结果令人鼓舞,并且揭示了从LRS到HRS的平滑非线性变化,反之亦然,因此对于神经形态应用很有用。

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