Shadows in the Spotlight: Casting Deep Learning Models to Unveil the Geometry of Printed Structures

Knowledge of the geometrical parameters of contact fingers is crucial for monitoring and optimizing solar cells. This paper introduces a modular approach based on deep learning (DL) for a full‐area quality inspection of the finger geometry. Our approach is designed for production use, as the underlying optical images can be captured in real time using a top‐light and a low‐angle laser illumination. The former provides information regarding the width of the metallized region (shading width), and the latter casts a shadow relevant for the computation of structural information as the core width, peak height, and cross‐sectional area. Our approach consists of (a) an image processing algorithm for automatic finger detection, (b) a DL model to extract the finger height profile from noisy shadow images, (c) a DL model for generating maps of the metallized regions and high‐resolution height images, and (d) a regression model to predict the geometrical parameters. Finally, we convert these parameters into quality maps for visualization and statistical analysis. On comparison with microscopic references, the model achieves a correlation coefficient of 0.93 and a mean absolute error of 20 μm ² for cross‐sectional areas ranging from 80 μm ² to 415 μm ² minimizing the need for offline microscopic measurements.