Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917594812907520 |
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| author | Nguyen, Binh Duong Steiner, Johannes Wellmann, Peter Sandfeld, Stefan |
| author_facet | Nguyen, Binh Duong Steiner, Johannes Wellmann, Peter Sandfeld, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer Nguyen, Binh Duong Steiner, Johannes Wellmann, Peter Sandfeld, Stefan Computer Vision and Pattern Recognition Materials Science Machine Learning 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. |
| title | Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer |
| topic | Computer Vision and Pattern Recognition Materials Science Machine Learning |
| url | https://arxiv.org/abs/2402.13353 |