Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs
This provides insights for materials science researchers studying ion beam effects on semiconductors, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of analyzing morphological properties of semiconductor microstructures by processing electron microscopy images into skeleton graphs and using a graph convolutional network for embedding, finding that irradiation angle has a greater impact than fluence on Ge surface morphology.
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.