Enhancing solar cell production line monitoring through advanced statistical analysis

Efficient monitoring of solar cell performance in high-volume production lines is crucial to ensure consistency and stability. However, this task faces challenges as many manufacturing processes introduce efficiency variations. This study proposes a method, based on lag sequential analysis, to monitor and evaluate these variations. The proposed method is based on the analysis of time-series electrical measurements (such as open-circuit voltage, short-circuit current, fill factor, and efficiency) to identify the degree of randomness, trace process-induced batch variations, and assess line stability. Real-time application of the method can flag anomalies. Furthermore, the suggested method can be extended to image analysis by extracting relevant features from time-series luminescence images, enabling the study of whether cell defects in manufacturing exhibit a random pattern or possess distinguishable characteristics. With its various possible applications, the proposed method has significant potential in enhancing solar cell production line monitoring systems, enabling early identification of production issues and process improvement by manufacturers.