预测中等压力等离子体行为的机器学习案例研究

Shadhin Hussain, David J. Lary, K. Hara, K. Bera, Shahid Rauf, M. Goeckner
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引用次数: 0

摘要

由于物理和化学过程在多个尺度上的相互作用,对中压电容耦合等离子体 (CCP) 系统等复杂系统的动态进行建模和预测仍然是一项挑战。一直以来,针对特定应用的优化都是通过对各种外部控制参数进行实验设计 (DOE) 研究来实现的。机器学习 (ML) 技术显示出 "预测 "传统 DOE 研究中未测试的工艺条件的潜力,从而更好地优化和控制等离子体工具。在本文中,我们使用标准 DOE 和 ML 预测来分析中压 CCP 系统中的 I-V 数据。我们证明了监督回归 ML 技术是推断数据的有用工具,即使等离子系统正在经历加热模式的转变,在本例中是从阿尔法模式到伽马模式的转变。控制参数的分类分析是 ML 技术的另一个可能应用,可用于系统控制。在这里,我们展示了在给定大量测量数据的情况下,模型可以识别原料气体中的气体比例,并在几乎所有情况下正确识别工作压力和电极间隙。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Case study in machine learning for predicting moderate pressure plasma behavior
Modeling and forecasting the dynamics of complex systems, such as moderate pressure capacitively coupled plasma (CCP) systems, remains a challenge due to the interactions of physical and chemical processes across multiple scales. Historically, optimization for a given application would be accomplished via a design of experiment (DOE) study across the various external control parameters. Machine learning (ML) techniques show the potential to “forecast” process conditions not tested in a traditional DOE study and thereby allow better optimization and control of a plasma tool. In this article, we have used standard DOE as well as ML predictions to analyze I-V data in a moderate-pressure CCP system. We have demonstrated that supervised regression ML techniques can be a useful tool for extrapolating data even when a plasma system is undergoing a transition in the heating mode, in this case from the alpha to gamma mode. Classification analysis of control parameters is another possible application of ML techniques that can be deployed for system control. Here, we show that given a large set of measured data, the models can identify the gas ratio in the feed gas as well as correctly identify the operating pressure and electrode gap in almost all the cases.
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