机器学习力场用于的热氧化
Lukas Cvitkovich1, Franz Fehringer1, Christoph Wilhelmer1
1Institute for Microelectronics, Technische Universität Wien, 1040 Wien, Austria.
The Journal of chemical physics
|October 10, 2024
概括
研究人员开发了一种机器学习力场 (MLFF),以准确模拟氧化. 这种新方法提高了对先进纳米电子设备的/二氧化接口的原子层次理解.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 半导体物理 半导体物理
背景情况:
- (Si) 和二氧化 (SiO2) 是半导体工业的基础.
- 纳米电子设备的缩小需要超薄的氧化物层和对Si/SiO2接口的更深入的理解.
- 经典力场缺乏准确性,而ab initio方法在计算上昂贵.
研究的目的:
- 训练机器学习力场 (MLFF) 来模拟基板的干热氧化.
- 在建模Si/SiO2接口时,实现精度和计算成本之间的平衡.
- 为研究界提供公开可用的MLFF.
主要方法:
- 密度函数理论 (DFT) 的计算用于为MLFF生成训练数据.
- 使用DFT生成的数据开发和训练了一种新的MLFF.
- 使用训练有素的MLFF进行了干燥热氧化过程的模拟.
主要成果:
- MLFF准确地复制了Si/SiO2接口上的原子配置,与初始模拟和实验数据一致.
- 与经典和反应力场相比,MLFF模拟显示了大大改善的结果.
- 开发的MLFF为模拟Si氧化提供了一种计算效率高且准确的方法.
结论:
- 机器学习力场代表了模拟半导体制造过程的重大进步.
- 经过培训的MLFF为理解和优化下一代电子产品中的Si/SiO2接口提供了宝贵的工具.
- 公共可用性MLFF促进进一步的纳米电子的研究和开发.
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