End-of-Line Binning of Full and Half-Cut Cells using Deep Learning on Electroluminescence Images

End-of-line binning of solar cells ensures optimal power output of photovoltaic modules, as well as identification of misprocessed cells. Currently, binning is performed using current-voltage measurements. This study proposes a deep learning framework to detect defective cells, predict cell efficiencies and bin the cells directly from electroluminescence imaging using a custom-made convolutional neural network. Already at this early stage, the network identifies defective cells with 93.7% accuracy and predicts cell efficiency within a mean error of 0.15% absolute efficiency. This framework is also applied to half-cut cells, providing a solution for post-cutting binning of this new structure. This study paves the way to deep learning applications in solar cell production lines and unlocks the potential of luminescence imaging as the ultimate end of line process monitoring and quality control tool.