A Computer Vision-Inspired Deep Learning Architecture for Virtual Metrology Modeling With 2-Dimensional Data

The rise of industry 4.0 and data-intensive manufacturing makes advanced process control (APC) applications more relevant than ever for process/production optimization, related costs reduction, and increased efficiency. One of the most important APC technologies is virtual metrology (VM). VM aims at exploiting information already available in the process/system under exam, to estimate quantities that are costly or impossible to measure. Machine learning (ML) approaches are the foremost choice to design VM solutions. A serious drawback of traditional ML methodologies is that they require a features extraction phase that generally limits the scalability and performance of VM solutions. Particularly, in presence of multi-dimensional data, the feature extraction process is based on heuristic approaches that may capture features with poor predictive power. In this paper, we exploit modern deep learning (DL)-based technologies that are able to automatically extract highly informative features from the data, providing more accurate and scalable VM solutions. In particular, we exploit DL architectures developed in the realm of computer vision to model data that have both spatial and time evolution. The proposed methodology is tested on a real industrial dataset related to etching, one of the most important semiconductor manufacturing processes. The dataset at hand contains optical emission spectroscopy data and it is paradigmatic of the feature extraction problem in VM under examination.