Domain-adaptive active learning for cost-effective virtual metrology modeling

Abstract

Virtual metrology (VM) is a promising solution for wafer-to-wafer quality monitoring in the semiconductor manufacturing process. VM alternates physical metrology with a prediction model trained using previous metrology data. Active learning can be used to build a VM model for new equipment efficiently with reduced metrology costs. However, conventional active learning is limited by a low prediction accuracy at its initial stage, which is referred to as the cold-start problem. In this study, we propose a domain-adaptive active learning method to address this issue. Using existing equipment as the source domain, the proposed method initializes the VM model through unsupervised domain adaptation from the source domain to the target domain. Active learning is then performed to iteratively update the VM model toward improving the prediction accuracy. Thus, the metrology cost required to obtain a VM model that satisfies the desired prediction accuracy for the target metrology task can be reduced. The effectiveness of the proposed method is demonstrated experimentally using real-world data from a semiconductor manufacturer.