Partial co-training for virtual metrology

Abstract

Virtual metrology is an important tool for industrial automation. To accurately build regression models for virtual metrology, we consider semi-supervised learning where labeled data are expensive to collect, but unlabeled data are abundant. In such a scenario, due to the scarcity of labeled data, traditional single-view learning methods face the risk of overfitting. To address the overfitting issue, we develop a Partial Co-training framework, which is an extension of the original co-training approach by means of an undirected probabilistic graphical model. Unlike other co-training techniques, this model creates a partial view by shrinking the original feature space, and makes use of this partial-view to provide guidance information for improving the complete-view model. Our approach is validated with data from two manufacturing applications. The results indicate that a consistent and robust estimation is achievable with very limited labeled data.