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.

中文翻译：

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多功能丝状电阻开关模型

忆阻器作为新兴的纳米电子器件在过去几年中已成功制造并用于非常规和神经形态计算系统。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，尽管量子力学过程是忆阻器操作的主要部分，但据我们所知，它们都没有被纯粹表征为量子力学。在本文中，我们采用量子力学方法为忆阻器工作周期中的电阻变化开发了一个完整而准确的丝状模型。更具体地说，我们应用量子游走来建模和计算形成灯丝的原子的运动，紧束缚哈密顿函数来捕获灯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，以加速我们的计算并充分提高模型的性能。我们的仿真结果成功地再现了忆阻器器件的电阻开关特性，与现有制造的器件实验数据相匹配，证明了所提出模型在多参数化方面的有效性和鲁棒性，并为其操作提供了新的有用的见解。我们通过图形处理单元 (GPU) 继续对整个模型进行并行化，以加速我们的计算并充分提高模型的性能。我们的仿真结果成功地再现了忆阻器器件的电阻开关特性，与现有制造的器件实验数据相匹配，证明了所提出模型在多参数化方面的有效性和鲁棒性，并为其操作提供了新的有用的见解。我们通过图形处理单元 (GPU) 继续对整个模型进行并行化，以加速我们的计算并充分提高模型的性能。我们的仿真结果成功地再现了忆阻器器件的电阻开关特性，与现有制造的器件实验数据相匹配，证明了所提出模型在多参数化方面的有效性和鲁棒性，并为其操作提供了新的有用的见解。