TY  - JOUR
AU  - Nguyen, Binh Duong
AU  - Steiner, Johannes
AU  - Wellmann, Peter
AU  - Sandfeld, Stefan
TI  - Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
JO  - MRS communications
VL  - 14
SN  - 2159-6859
CY  - Berlin
PB  - Springer
M1  - FZJ-2024-03488
SP  - 612-627
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001233735600001
DO  - DOI:10.1557/s43579-024-00563-2
UR  - https://juser.fz-juelich.de/record/1026670
ER  -