Enhancing Scalability of Virtual Metrology: A Deep Learning-Based Approach for Domain Adaptation

Abstract

One of the main challenges in developing Machine Learning-based solutions for Semiconductor Manufacturing is the high number of machines in the production and their differences, even when considering chambers of the same machine; this poses a challenge in the scalability of Machine Learning-based solutions in this context, since the development of chamber-specific models for all equipment in the fab is unsustainable. In this work, we present a domain adaptation approach for Virtual Metrology (VM), one of the most successful Machine Learning-based technology in this context. The approach provides a common VM model for two identical-in-design chambers whose data follow different distributions. The approach is based on Domain-Adversarial Neural Networks and it has the merit of exploiting raw trace data, avoiding the loss of information that typically affects VM modules based on features. The effectiveness of the approach is demonstrated on real-world Etching.