Combining unsupervised and supervised learning in microscopy enables
  defect analysis of a full 4H-SiC wafer
Binh Duong Nguyen Johannes Steiner Peter Wellmann Stefan Sandfeld
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 techniques for
creating a robust and accurate, automated image analysis pipeline. This allows
for extracting the type and position of all defects in a microscopy image of a
KOH-etched 4H-SiC wafer that was stitched together from approximately 40,000
individual images.