An Automatic Condition Detection Approach for Quality Assurance in Solar Cell Manufacturing Processes
Abstract
Solar conversion efficiency (SCE) is a critical quality metric in solar cell production. This paper presents an automatic approach for detecting condition changes in the multistage solar cell manufacturing process, using temperature and reflectance profiles during epitaxy layer growth. A likelihood ratio test is applied for early detection of changes in SCE, allowing timely process adjustments and remedies.
Type
Publication
IEEE Robotics and Automation Letters
Juan Du
Assistant Professor
My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.