Chen Shan

Statistical key variable analysis and model-based control for improvement performance in a deep reactive ion etching process

This paper proposes to develop a data-driven vias 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 vias 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.