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.
Cite
@article{arxiv.2508.07850,
title = {Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs},
author = {Noriko Nitta and Rei Miyata and Naoto Oishi},
journal= {arXiv preprint arXiv:2508.07850},
year = {2025}
}
Comments
CV4MS: Computer Vision for Materials Science, Workshop in conjunction with the IEEE/CVF ICCV 2025