多功能丝状电阻开关模型
Versatile Filamentary Resistive Switching Model
摘要：
忆阻器作为一种新兴的纳米电子器件，近年来已经成功地制备并应用于非传统的类脑计算系统中。已经开发了几种基于行为或物理的模型来解释它们的操作并优化它们的制造参数。所有现有的忆阻器模型都是准确性，通用性和现实性之间的权衡，但是，据我们所知，它们中没有一个是纯粹的量子力学特征，尽管量子力学过程是忆阻器操作的主要部分。本文采用量子力学方法建立了一个完整且准确的记忆电阻器工作周期内电阻变化的细丝模型。更具体地说，我们应用量子行走来建模和计算形成细丝的原子的运动，紧束缚哈密顿来捕获细丝结构和非平衡格林函数（NEGF）方法来计算设备的电导。此外，我们通过图形处理单元（GPU）对整个模型进行了并行化，以加快计算速度并充分提高模型的性能。我们的模拟结果成功地再现了忆阻器件的阻变特性，与现有的制造设备的实验数据相匹配，证明了所提出的模型在多参数化方面的有效性和鲁棒性，并提供了一个新的和有用的洞察其操作。
英文摘要：
Memristors as emergent nano-electronic devices have been successfully fabricated and used in non-conventional and neuromorphic computing systems in the last years. Several behavioral or physical based models have been developed to explain their operation and to optimize their fabrication parameters. All existing memristor models are trade-offs between accuracy, universality and realism, but, to the best of our knowledge, none of them is purely characterized as quantum mechanical, despite the fact that quantum mechanical processes are a major part of the memristor operation. In this paper, we employ quantum mechanical methods to develop a complete and accurate filamentary model for the resistance variation during memristor's operating cycle. More specifically, we apply quantum walks to model and compute the motion of atoms forming the filament, tight-binding Hamiltonians to capture the filament structure and the Non-Equilibrium Green's Function (NEGF) method to compute the conductance of the device. Furthermore, we proceeded with the parallelization of the overall model through Graphical Processing Units (GPUs) to accelerate our computations and enhance the model's performance adequately. Our simulation results successfully reproduce the resistive switching characteristics of memristors devices, match with existing fabricated devices experimental data, prove the efficacy and robustness of the proposed model in terms of multi-parameterization, and provide a new and useful insight into its operation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
