A Systematic Literature Review of Machine Learning Applications for Process Monitoring and Control in Semiconductor Manufacturing

Due to diversity and many possibilities for data collection in semiconductor manufacturing, various complex machine learning approaches exist for different process steps. However, a systematic overview of these approaches is missing. This study, therefore, systematically reviews machine learning applications for process monitoring and control in semiconductor manufacturing based on peer-reviewed literature. To structure the review, we use the wafer fabrication plant-wide framework for process monitoring and control and the framework of continuous process improvement based on machine learning technique. We identify respective application areas and future research needs of machine learning for process monitoring and control in semiconductor manufacturing.