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Dislocation defects in SiC wafers, such as TSD, TED, and BPD, critically limit device yield and reliability, while their growth mechanisms remain insufficiently understood. This work presents a fast, non-destructive inspection method based on transient absorption spectroscopy and AI algorithms for accurate defect detection and classification. The study reveals distinct growth modes of threading dislocations and identifies transformation relationships between dislocations and basal plane defects. These findings provide new insights into SiC crystal growth and support improved defect control and wafer quality in semiconductor manufacturing.
