Half and full solar cell efficiency binning by deep learning on electroluminescence images

Abstract

Abstract End‐of‐line characterization of solar cells is necessary to filter out defective cells and bin cells to avoid power mismatch loss in photovoltaic modules. Current–voltage testers, used by almost any photovoltaic company and research laboratory, are costly to maintain and to adapt to recent and predicted morphological changes in solar cells: larger and thinner wafers, half or shingled cells, a wide range of busbar layouts, and more. In this study, we challenge this fundamental technique and propose to bin solar cells and detect defective cells based on a deep learning analysis of their electroluminescence images. The use of electroluminescence imaging addresses the above‐mentioned limitations of the current–voltage technique, as well as allowing faster measurements as it avoids any capacitance effects. By introducing LumiNet, a convolutional neural network end‐to‐end framework, solar cell efficiency bins can be accurately predicted from electroluminescence imaging with a mean error similar to that obtained by current–voltage measurements. The proposed framework is validated on several state‐of‐the‐art mono‐crystalline silicon solar cell structures. We show that photovoltaic modules fabricated using the proposed method would have similar mismatch loss as the traditional current–voltage binning. We then demonstrate the method on half‐cut silicon solar cells. Predicting the half‐cut cell efficiencies, from the deep learning framework, enables manufacturers to assess post‐cutting damages and reassess their binning strategy before module assembly. Furthermore, the deep learning framework is shown to work well even on datasets that have not been previously seen. The trained deep learning LumiNet models' structure and weight are shared with the community to accelerate the adaptation of deep learning for luminescence image analysis in the photovoltaic industry.

Topics & Concepts