Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

arXiv:2402.13353v1 Announce Type: cross
Abstract: Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms as well as tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data that is produced by experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining …

abstract analysis arxiv cond-mat.mtrl-sci cs.cv cs.lg defects image images materials microscopy processes production production processes segmentation semiconductor supervised learning tasks type types understanding unsupervised