DBAM: Making Virtual Metrology/Soft sensing with time series data scalable through Deep Learning

Machine Learning (ML) based technologies, like Virtual Metrology (VM)/Soft Sensing, Predictive Maintenance and Fault Detection, have been successfully applied in the past recent years in data intensive manufacturing industries, like semiconductor manufacturing, to improve process monitoring and related operations. Standardization and alignment over multiple equipment is a key element to ensure industry-wide adoption and scalability for ML-based technologies in complex production environment. In this work we address the topic of VM/Soft Sensing – a particular ML-based technology for process control – in the context of equipment matching and scalability. We present a Deep Learning-based domain adaptation approach, called DANN-Based Model Alignment (DBAM), that provides a common VM model for two identical-in-design systems whose data are following different distributions. The proposed approach has the merit of (i) exploiting directly raw sensor data (that typically present themselves in the form of time series) and (ii) offering interpretability of the features. The proposed approach is compared against other approaches in the literature for VM/Soft Sensing on a real-world case study from semiconductor manufacturing.