Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression

Abstract

Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also supports improved decision-making, particularly in process control and quality assurance. To exploit the consecutive nature of OES data, we propose a prediction method based on a functional approach using multivariate functional partial least squares regression, coupled with dimension reduction and a novel outlier detection technique via functional independent component analysis. The proposed approach improves prediction performance by capturing the continuous nature of OES data and effectively extracting the components that describe the data structure. Numerical experiments, including simulation studies and real-world applications of OES data, demonstrate the effectiveness of the proposed method through superior prediction performance, as evidenced by low RMSE and MAE values, particularly in the presence of outliers.