Exploring Machine Learning for Semiconductor Process Optimization: A Systematic Review

As machine learning continues to find applications, extensive research is currently underway across various domains. This study examines the current methodologies of machine learning 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 machine learning-based semiconductor process optimization. These investigations employ machine learning techniques such as feature extraction, feature selection, neural network architecture, and 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 papers based on the methods and applications employed, summarizing the primary findings. Furthermore, we discuss the general conclusions of several studies. Overall, the reviewed literature suggests that machine learning-based semiconductor manufacturing is rapidly gaining popularity and advancing at a swift pace. IEEE