Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

The casting mono-like silicon (Si) grown by directional solidification (DS) is promising for high-efficiency solar cells. However, high dislocation clusters around the top region are still the practical drawbacks, which limit its competitiveness to the monocrystalline Si. To optimize the DS-Si process, we applied the framework, which integrates the growing experiments, transient global simulations, artificial neuron network (ANN) training, and genetic algorithms (GAs). First, we grew the Si ingot by the original recipe and reproduced it with transient global modeling. Second, predictions of the Si ingot domain from different recipes were used to train the ANN, which acts as the instant predictor of ingot properties from specific recipes. Finally, the GA equipped with the predictor searched for the optimal recipe according to multi-objective combination, such as the lowest residual stress and dislocation density. We also implemented the optimal recipe in our mono-like DS-Si process for verification and comparison. According to the optimal recipe, we could reduce the dislocation density and smooth the growth rate during the Si ingot growing process. Comparisons of the growth interface and grain boundary evolutions showed the decrease of the interface concavity and the multi-crystallization in the top part of the ingot. The well-trained ANN combined with the GA could derive the optimal growth parameter combinations instantly and quantitatively for the multi-objective processes.