Study on electrical activity of grain boundaries in silicon through systematic control of structural parameters and characterization using a pretrained machine learning model

Abstract

We report on the effects of grain boundary (GB) structures on the carrier recombination velocity at GB (vGB) in multicrystalline Si (mc-Si). The fabricated artificial GBs and an originally developed machine learning model allowed an investigation of the effect of three macroscopic parameters, misorientation angle α for Σ values, asymmetric angle β, and deviation angle θ from the ingot growth direction. Totally, 13 GBs were formed by directional solidification using multi-seeds with controlled crystal orientations. vGB was evaluated directly from photoluminescence intensity profiles across GBs using a pre-trained machine learning model, which allowed a quantitative and continuous evaluation along GBs. The evaluation results indicated that the impact of θ on vGB would be relatively large among the three macroscopic parameters. In addition, the results for the Σ5 and Σ13 GBs suggested that the minimum vGB would be related to the GB energy. These results were discussed in terms of the complexity of the local reconstruction of GB structures. The deviation would make a more complex reconstructed GB structure with local distortion, resulting in an increase in the electrical activity of GBs. The obtained knowledge will contribute to improving various polycrystalline materials through a comprehensive understanding of the relationship between GB structures and their properties.