基于TiO2的纳米结构忆阻器在RRAM和神经形态学应用中的模拟方法
TiO2 based Nanostructured Memristor for RRAM and Neuromorphic Applications: A Simulation Approach
摘要：
我们报告模拟的纳米结构忆阻器件使用分段线性和非线性窗口函数的RRAM和神经形态的应用。忆阻器的线性漂移模型已经被开发用于仿真目的，线性和非线性窗口函数作为数学和脚本基础。结果表明，分段线性窗函数可以很好地模拟与RRAM应用相关的忆阻器特性。然而，非线性窗函数只能在控制参数较低的情况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函数来仿真忆阻器的仿真模型。有趣的是，所提出的窗函数可扩展到f（x）=1，并且在较高的控制参数幅度下表现出非线性行为。此外，所提出的非线性窗函数的仿真结果是令人鼓舞的，并揭示了从LRS到HRS的平滑的非线性变化，反之亦然，因此对神经形态应用是有用的。
英文摘要：
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.
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