Dynamic sparse PCA: a dimensional reduction method for sensor data in virtual metrology

Abstract

Virtual metrology (VM) is the ideal technique for predicting the quality of every wafer and reducing overall inspection costs in semiconductor manufacturing. The data from numerous monitoring sensors in semiconductor production poses challenges in terms of data dimensionality. Thus, dimensionality reduction techniques are required before building the prediction model in VM. Additionally, to reduce the sensor cost, it is crucial to determine the sensors that contribute significantly to the output of the prediction model. Therefore, in this paper, we propose a dimensionality reduction technique called dynamic sparse principal component analysis to establish global sparsity in the first few principal components. The proposed method seeks extracted features that involve a limited number of sensors in the VM to reduce sensor cost and increase the interpretability of the features while reasonably capturing data variability. The experimental results demonstrate that the proposed method is not sensitive to the scale of the problem and can solve the large-scale high-dimensional problems that are commonly encountered in semiconductor manufacturing.