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

Nguyen BD, Steiner J, Wellmann P, Sandfeld S (2024)

Publication Type: Journal article

Publication year: 2024

Journal

MRS Communications Cambridge University Press

Book Volume: 14

Pages Range: 612-627

Journal Issue: 4

DOI: 10.1557/s43579-024-00563-2

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: (Figure presented.)

Authors with CRIS profile

Johannes Steiner Professur für Werkstoffwissenschaften (Werkstoffe der Elektrotechnik) Peter Wellmann Professur für Werkstoffwissenschaften (Werkstoffe der Elektrotechnik)

Involved external institutions

Forschungszentrum Jülich / Research Centre Jülich (FZJ) DE Germany (DE)

How to cite

APA:

Nguyen, B.D., Steiner, J., Wellmann, P., & Sandfeld, S. (2024). Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer. MRS Communications, 14(4), 612-627. https://doi.org/10.1557/s43579-024-00563-2

MLA:

Nguyen, Binh Duong, et al. "Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer." MRS Communications 14.4 (2024): 612-627.

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