Cation-based (or electrochemical) resistive memory devices are gaining increasing interest in neuromorphic applications due to their capability to emulate the dynamic behavior of biological neurons and synapses. The utilization of such devices in neuromorphic systems necessitates a reliable physical model for the resistance switching mechanism, which is based on the formation and dissolution of a conductive filament in a thin dielectric layer, sandwiched between two metal electrodes. We propose a comprehensive model to simulate the evolution of the filament geometry under the effect of both surface diffusion caused by curvature gradient and electromechanical stress, and mass injection due to electrodeposition of cations. The model has been implemented in a C++ platform using a level-set approach based on a mixed finite element formulation, enriched by a mesh adaptation strategy to accurately and efficiently track the evolution of the filament shape. The numerical scheme is initially validated on various benchmark case studies. We then simulate the growth and self-dissolution of the filamentary geometry, incorporating an electrical model allowing a comparison with conventional cation-based memories. The simulations showcase filament formation under varying applied voltages and filament dissolution under different initial resistances.

中文翻译：

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丝状阳离子电阻存储器件中电化学和表面扩散效应的建模和仿真


基于阳离子（或电化学）的电阻式存储器件由于能够模拟生物神经元和突触的动态行为，因此在神经形态应用中越来越受到关注。在神经形态系统中使用此类设备需要一个可靠的电阻切换机制物理模型，该模型基于夹在两个金属电极之间的薄介电层中导电丝的形成和溶解。我们提出了一个综合模型来模拟在曲率梯度和机电应力引起的表面扩散以及阳离子电沉积引起的质量注入的影响下灯丝几何形状的演变。该模型已在 C++ 平台上实现，使用基于混合有限元公式的水平集方法，并通过网格自适应策略进行丰富，以准确有效地跟踪细丝形状的演变。该数值方案最初在各种基准案例研究中得到验证。然后，我们模拟丝状几何形状的生长和自溶解，并结合电气模型，以便与传统的基于阳离子的存储器进行比较。模拟展示了不同施加电压下细丝的形成以及不同初始电阻下细丝的溶解。