Regression Methods for Predicting the Product’s Quality in the Semiconductor Manufacturing Process*

The quality of production in the wafer manufacturing process cannot be always monitored by metrology tools because physical measurements are very expensive. Instead of conducting costly quality tests, it is desirable to predict the wafer quality Regression models are useful to build such a predictor by using the production equipment data and a set of wafer quality measurements. As the semiconductor manufacturing process consists of a huge amount of data that are correlated and very few quality measurements, Ordinary Least Squares (OLS) regression fails in predicting the wafer’s quality. Regression methods dealing with multicollinear high-dimensional input data are required. In this paper, a survey of regularized linear regression methods based on feature reduction and variable selection methods is presented. These methods are applied to predict the wafer quality based on the production equipment data, then compared. Regression parameter optimization and model selection are performed and evaluated via cross validation, using the Mean Squared Error (MSE). Our results indicate that reducing the predictor’s dataset will improve the model robustness and the prediction accuracy.