Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. The approach combines predictive modeling, process optimization, and explainable AI to support photovoltaic manufacturing. The model uses over 100,000 industrial data points to make data-based decisions and enable intelligent automation in photovoltaic manufacturing. A new machine learning methodology for optimizing process conditions in solar cell manufacturing was introduced in a recent study published in Energy and AI, showing efficiency and rapidity compared to traditional methods. The research group also developed a machine learning model for predicting sheet resistance in solar cell manufacturing processes, offering reliability and interpretability for potential application in various industrial processes.
AI now can now predict solar cell efficiency from wafer quality
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