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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07850 |
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| _version_ | 1866909732189503488 |
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| author | Nitta, Noriko Miyata, Rei Oishi, Naoto |
| author_facet | Nitta, Noriko Miyata, Rei Oishi, Naoto |
| contents | In this paper, electron microscopy images of microstructures formed on Ge surfaces by ion beam irradiation were processed to extract topological features as skeleton graphs, which were then embedded using a graph convolutional network. The resulting embeddings were analyzed using principal component analysis, and cluster separability in the resulting PCA space was evaluated using the Davies-Bouldin index. The results indicate that variations in irradiation angle have a more significant impact on the morphological properties of Ge surfaces than variations in irradiation fluence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs Nitta, Noriko Miyata, Rei Oishi, Naoto Computer Vision and Pattern Recognition In this paper, electron microscopy images of microstructures formed on Ge surfaces by ion beam irradiation were processed to extract topological features as skeleton graphs, which were then embedded using a graph convolutional network. The resulting embeddings were analyzed using principal component analysis, and cluster separability in the resulting PCA space was evaluated using the Davies-Bouldin index. The results indicate that variations in irradiation angle have a more significant impact on the morphological properties of Ge surfaces than variations in irradiation fluence. |
| title | Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.07850 |