Efficient Classification via Partial Co-Training for Virtual Metrology

Developing accurate and cost-effective classification techniques to facilitate virtual metrology is a critical task for modern manufacturing. In this paper, we consider the scenario in which labeling data is expensive, causing a shortage of labeled data. As a consequence, conventional classification methods suffer from a high risk of overfitting. To address this issue, we develop a novel semi-supervised classification method, namely Partial Co-training with Logistic Regression (PCT-LR). PCT-LR finds a subset of the original features to generate a partial view, and uses this partial view to provide side information to support the complete view that includes all features. Both views are co-optimized in a Bayesian inference with a Gaussian process prior and a logistic regression classifier. The proposed method is validated with two industrial examples. Experiment results suggest that the amount of required labeled data can be reduced by up to 18% without loss in accuracy.