Statistical key variable analysis and model-based control for improvement performance in a deep reactive ion etching process深度反应离子蚀刻过程的统计关键变量分析与推理控制

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摘要：

This paper proposes to develop a data-driven via's depth estimator of the deep reactive ion etching process based on statistical identification of key variables. Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology (VM) model building process. With the available on-line VM model, the model-based controller is hence readily applicable to improve the quality of a via's depth. Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method can decrease the MSE from 2.2×10-2 to 9×10-4 and has great potential in improving the existing DRIE process.

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关键词：

Theoretical or Mathematical/ manufacturing processes sputter etching statistical analysis virtual manufacturing/ statistical key variable analysis deep reactive ion etching process depth estimator statistical identification extraction algorithms high-dimensional data virtual metrology model on-line VM model model-based controller depth quality operational data industrial manufacturing process DRIE process/ E1520N Surface treatment and coating techniques E3644A Semiconductor industry E0210J Statistics E1510 Manufacturing systems