Deep Learning-based Sequence Modeling for Advanced Process Control in Semiconductor Manufacturing

Semiconductor manufacturing is one of the most data-intensive industries in manufacturing. Many so-called Advanced Process Control (APC) approaches based on equipment or process data have been presented over the years to improve quality and efficiency, reduce waste and increase energy savings. While on the one hand, intensive production leads to large amount of data available for the development of Machine Learning-based technologies, on the other hand, the high-mix production, multiple processes and machines lead researchers and developers to deal with ’small data’ scenarios. In this context, domain adaptation approaches are highly relevant to enhance scalability. In this work, we are extending a state-of-the-art Deep Learning architecture called Domain Adversarial Neural Network based Alignment Model (DBAM) by considering new sequence learning layers. In the real-world case study, the proposed architecture is shown to achieve higher accuracy, allow for better management in the absence of large amounts of data and make previously trained models reusable in similar scenarios.