For advanced equipment control, two schemata of real-time fault detection were performed using machine learning algorithms in silicon etching in SF ₆ /O ₂ /Ar plasma. Fault detection and classification is investigated with the plasma state information with optical emission spectroscopy (OES) data to find the root cause of the anomaly in the process parameters. Fault detection and control is also demonstrated to predict the shift of the process parameter along the amount of process gas flow rate injected into the chamber, considering a fault. Especially, plasma information (PI), such as electron temperature and electron density, was derived from OES data into equation-based corona model. These were utilized to evaluate which process parameter is the most significantly affecting on the performance of the established model through Shapley value in fault detection and control. By the combination of isolation forest algorithm for finding the plasma abnormalities in real time and Adaboost algorithm for classifying root causes of faults, the suggested algorithm could accurately detect the root cause. DeepSHAP algorithm helped not only the prediction of gas flow rate, but PI was identified as critical parameter, interpreting the model through Shapley value. We propose a new multi-function integrated algorithm by the ensemble algorithms.

中文翻译：

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等离子信息在基于机器学习的故障检测和分类中用于先进设备控制

对于先进的设备控制，在 SF ₆ /O ₂硅蚀刻中使用机器学习算法执行了两个实时故障检测模式 /Ar等离子体。利用等离子体状态信息和光学发射光谱 (OES) 数据研究故障检测和分类，以找出工艺参数异常的根本原因。还演示了故障检测和控制，以预测工艺参数随着注入腔室的工艺气体流速的变化，考虑到故障。特别是，等离子体信息 (PI)，如电子温度和电子密度，是从 OES 数据导出到基于方程的电晕模型中。这些被用来通过故障检测和控制中的 Shapley 值来评估哪个过程参数对所建立模型的性能影响最显着。通过将实时发现等离子体异常的隔离森林算法和用于故障根源分类的Adaboost算法相结合，该算法能够准确地检测出故障根源。DeepSHAP 算法不仅有助于预测气体流量，而且 PI 被确定为关键参数，通过 Shapley 值解释模型。我们通过集成算法提出了一种新的多功能集成算法。