A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals 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 model. Thus, this research method reveals 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.
Keywords: ab initio calculation; computational fluid dynamics simulation; dislocation clusters; finite element stress analysis; grain boundaries; machine learning; multiscale structural characterizations.
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