{"ID":2883680,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07850","arxiv_id":"2508.07850","title":"Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs","abstract":"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.","short_abstract":"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 clust...","url_abs":"https://arxiv.org/abs/2508.07850","url_pdf":"https://arxiv.org/pdf/2508.07850v1","authors":"[\"Noriko Nitta\",\"Rei Miyata\",\"Naoto Oishi\"]","published":"2025-08-11T11:10:07Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}