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

MRS Communications, vol. 14, pp. 612–627

Abstract

Abstract Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms and 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 from experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques to create a robust and accurate, automated image analysis pipeline for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer. Graphical abstract

Authors 2

1. Johannes Steiner

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Crystal Growth Lab, Materials Department 6, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91058, Germany

2. Peter J. Wellmann

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Crystal Growth Lab, Materials Department 6, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91058, Germany