Multiscale Transport Modeling: From Process Plasmas to Resistive Switching Devices (C05)

开发并实现了不同时间和长度尺度上的一致模型，从而实现了从记忆器件的加工到其电性能的连续模拟。通过机器学习回归模型实现不同物理模型的耦合。除了有助于对不同时间和长度尺度的物理学的基本理解外，该项目还将为CRC内神经形态电路的记忆器件的应用处理和选择提供建议。

Consistent models on different time and length scales are developed and implemented, which enable a continuous simulation from the processing of memristive devices to their electrical properties. The coupling of the different physical models is realized by a machine learning regression model. In addition to contributing to the fundamental understanding of physics on the different time and length scales, the project will provide suggestions for application-oriented processing and selection of memristive devices for neuromorphic circuits within the CRC.