Automated efficiency loss analysis by luminescence image reconstruction using generative adversarial networks

Identifying solar cell efficiency shortfalls in production lines is crucial to troubleshoot and optimize manufacturing processes. With the adoption of luminescence imaging as a key end-of-line characterization tool, a wealth of information is available to evaluate cell performance and classify defects, suitable for user input-free deep-learning analysis. We propose an automated reconstruction method, based on state-of-the-art generative adversarial networks, to remove defective regions in luminescence images. The reconstructed and original images are compared to estimate the efficiency loss. The method is validated on intentionally damaged cells by reconstructing defect-free images and then predicting the efficiency loss. The method can differentiate between different types of defects and pinpoint the defects that lead to the highest efficiency shortfall, enabling manufacturers to troubleshoot production lines in a fast and cost-effective manner. The proposed approach unlocks the potential of luminescence imaging as an effective end-of-line characterization tool.