用机器学习加速高深宽比结构中等离子体原子层沉积优化
Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features
- 基于仿真数据训练神经网络预测沉积饱和时间
- 仅需两次不完全沉积实验，误差小于10%
- 可推广至原子层刻蚀及复杂结构，适合芯片制造研究者
本文探索了代理模型在高深宽比结构中等离子体增强原子层沉积（PEALD）过程优化中的应用。在等离子体工艺如PEALD和原子层刻蚀中，表面复合常主导等离子体物种与表面的反应，导致实现完全共形所需暴露时间过长。利用基于PEALD仿真的合成数据，我们训练人工神经网络，根据部分覆盖条件下的截面厚度数据预测饱和时间。结果表明，仅需两次欠饱和条件下的实验，即可将饱和时间预测值控制在真实值的10%以内。另一代理模型用于判断表面复合是否主导等离子体-表面相互作用，准确率达99%。这证明机器学习可为微电子领域中PEALD工艺的优化提供新路径。该方法易于扩展至原子层刻蚀及更复杂结构。
原文摘要 · Abstract (English)
In this work we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching, surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma-surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to atomic layer etching and more complex structures.
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