金属/氧化物/金属导电桥接随机存取存储器单元的多尺度建模：从从头计算到有限元计算
Multiscale Modeling of Metal/Oxide/Metal Conductive Bridging Random Access Memory Cells: from Ab Initio to Finite Element Calculations
AI总结：
本文提出了一种多尺度模拟框架，用于计算CBRAM单元的I-V特性并分析其电阻开关行为，结合从头计算和有限元方法，提升建模精度并优化存储单元设计。
摘要：
我们提出了一种多尺度模拟框架，用于计算金属/氧化物/金属结构的电流-电压（I-V）特性，这些结构构成了导电桥接随机存取存储器（CBRAM）单元的核心，并揭示其电阻开关特性。该方法依赖于一个有限元模型，其输入材料参数要么来自从头计算，要么来自机器学习的实证计算。所采用的技术范围从分子动力学和受迫弹性带到电子和热量子输运。这种方法大大减少了所需的拟合参数数量，并使最终的建模环境比传统方法更准确。开发的计算框架随后应用于研究Ag/a-SiO2/Pt CBRAM，能够很好地再现实验数据。此外，通过考虑各种单元几何形状评估了焦耳加热的相关性。发现自加热在具有几纳米直径的导电丝的设备中以及在电流浓度为十微安范围时表现出来。通过提出的方法，现在可以探索尚未制造的存储单元的潜力，并可靠地优化其设计。
英文摘要：
We present a multiscale simulation framework to compute the current vs. voltage (I-V ) characteristics of metal/oxide/metal structures building the core of conductive bridging random access memory (CBRAM) cells and to shed light on their resistance switching properties. The approach relies on a finite element model whose input material parameters are extracted either from ab initio or from machine-learned empirical calculations. The applied techniques range from molecular dynamics and nudged elastic band to electronic and thermal quantum transport. Such an approach drastically reduces the number of fitting parameters needed and makes the resulting modeling environment more accurate than traditional ones. The developed computational framework is then applied to the investigation of an Ag/a-SiO2/Pt CBRAM, reproducing experimental data very well. Moreover, the relevance of Joule heating is assessed by considering various cell geometries. It is found that self-heating manifests itself in devices with thin conductive filaments with few-nanometer diameters and at current concentrations in the 10s-microampere range. With the proposed methodology it is now possible to explore the potential of not-yet fabricated memory cells and to reliably optimize their design.
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