Knowledge-Infused Process Monitoring for Quality Improvement in Solar Cell Manufacturing Processes

Abstract

Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing, is influenced by the epitaxy stage. This study proposes a knowledge-infused monitoring strategy, using a customized nonlinear model to capture key information from reflectance signals and rank SCE-correlated features for effective online process monitoring.

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.