Early stage quality assessment in silicon ingots from mdp brick characterization

Feedback on the material quality of silicon ingots is highly beneficial in the photovoltaic production chain. It is crucial for crystal growers to improve the quality and optimize the crystallization process. Moreover, for solar cell manufacturers, knowing the quality beforehand helps to sort out the bad quality material thereby reducing the costs and enhancing the total yield. Therefore, rating material quality already on the brick level is highly valuable for the effective optimization of the value chain in both directions. In this paper, we propose a method to classify the silicon bricks based on their electrical quality. Due to our comprehensive data set and feature detection, the model is capable to predict the quality of even edge and corner bricks of the ingot. We introduce a novel feature extraction method to quantify quality-related features from spatially-resolved microwave-detected photoconductivity (MDP) brick measurements. Further, a machine-learning-based prediction model is developed to predict the open-circuit voltage (Voc) of solar cells from these features. A comparative analysis for brick quality estimation for inner and outer bricks of high-performance multi (HPM) and cast-mono (CM) silicon bricks is provided. The best mean absolute error in prediction achieved for HPM and CM materials is 3.1 mV and 4.8 mV, respectively.