Exploring Machine Learning for Semiconductor Process Optimization: A Systematic Review

Abstract

As machine learning (ML) continues to find applications, extensive research is currently underway across various domains. This study examines the current methodologies of ML being investigated to optimize semiconductor manufacturing processes. Our research involved searching the SPIE Digital Library, IEEE Xplore, and ArXiv databases, identifying 58 publications in the field of ML-based semiconductor process optimization. These investigations employ ML techniques such as feature extraction, feature selection, and neural network architecture are analyzed using different algorithms. These models find applications in advanced process control, virtual metrology, and quality control, critical aspects in semiconductor manufacturing for enhancing throughput and reducing production costs. We categorize the articles based on the methods and applications employed, summarizing the primary findings. Furthermore, we discuss the general conclusion of several studies. Overall, the reviewed literature suggests that ML-based semiconductor manufacturing is rapidly gaining popularity and advancing at a swift pace.