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 processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.