Spatially Resolved Material Quality Prediction Via Constrained Deep Learning

Novel material classes for solar cell production e.g. high performance multicrystalline silicon or epitaxially grown wafers have a huge impact on solar cell performance. A speedup of these developments calls for a rapid assessment of the material quality in the as-cut stage already. This work introduces a generic architecture for the material rating of wafers in terms of solar cell quality. Our approach allows for a simultaneous prediction of the open-circuit voltage of the solar cell and the image of the dark-saturation current density (j
₀
) from photoluminescence images of as-cut wafers. In the sense of theory-guided data-analysis, we combine a data-driven machine learning approach with known physical constraints, here given by the one-diode equation. Due to this statistical optimization, our method derives the j
₀
values for the occluded regions beneath the busbar. From the derived j
₀
values, we also evaluate the impact of material-related defects and grid metallization structure on the dark-saturation current density.