Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling The Microscopic Root Cause of Dislocation Generation.

We conducted a comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, we were able to build a realistic three-dimensional (3D) model that includes the generation point of dislocation clusters. Finite element stress analysis on the 3D model coupled with crystal growth simulation revealed inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen-Alexander-Sumino (HAS) model. Thus, our research method revealed the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials. (198 words) This article is protected by copyright. All rights reserved.