Measurement images of solar cells contain information about their material‐ and process‐related quality beyond current–voltage characteristics. This information is currently only partially used because most algorithms look for human‐defined image features or defects. Herein, a purely data‐driven method is proposed to derive the essential image information in terms of the electrical quality within a comprehensive and meaningful representation. This representation is denoted as the empirical digital twin of the cell. Using it, solar cells can be classified according to their defects visible in the measurement images. For this purpose, a human‐in‐the‐loop approach to efficiently develop a classification scheme is presented. Therefore, a convolutional neural network combining various measurement data of a sample by correlating them with quality parameters is designed. The digital twin is an intermediate representation of the network capturing the quality‐relevant defect signatures from the images. Human experts can analyze this representation space to identify defect clusters that relate to different process errors, such as finger interruptions and shunts. How the representations are usable to derive sorting criteria for quality inspection is shown. Finally, how the empirical digital twin and the sorting scheme can be used for segmenting the defects without additional label effort is demonstrated.
The current–voltage measurement is the most important measurement in solar cell quality control. As the contacting process of cells results in mechanical stress and consumes a significant amount of measurement time, this work presents an IV characterization based on contactless measurements only. An empirical model is introduced that can derive the full IV curve and IV parameters as the open‐circuit voltage, short‐circuit current density, fill factor, and efficiency. As a basis, a series of photoluminescence and contactless electroluminescence images and spectral reflectance measurements are used. An advantage of the model's convolutional neural network design lies in the semantic compression of local image structures across the input data. Within an ablation study, it is shown that the empirical model is well suited to combine these data sources, which is the optimal input configuration for contactless IV derivation. The accuracy, e.g., with an error in efficiency of
0.035
%
abs
and correlation of over 99%, is similar to comparing two contacting IV measurement devices. The contactless IV curves also have a close fit to their contacted counterparts. Within simulations on module level, it is demonstrated that contactless binning performs as well as contacting binning and does not result in any additional mismatch loss.
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