Shingle Cell IV Characterization Based on Spatially Resolved Host Cell Measurements

Each solar cell is characterized at the end-of-line by means of current-voltage (IV ) measurements, except shingle cells, because the measurement effort for them would be multiplied. Therefore, the host cell quality is adopted for all resulting shingles. This leads to material loss due to incorrectly rejected shingles, inaccurate binning leading to increased mismatch losses, and lack of process monitoring. In host measurement images, such as electroluminescence or photoluminescence measurements, all shingles are visible along with their properties. Within the experiments, a deep learning model has been optimized that can process these images and determine IV parameters like efficiency or fill factor, IV curves and binning classes with high accuracy. For example, efficiency can be determined with an error of 0.06%abs. The binning improves by 13%abs compared to the industry standard which results in lower mismatch losses and higher output power on module level as demonstrated in module simulations. Also IV curves of defective and defect-free shingle cells can be derived with good agreement to the actual measurements.