Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs
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Abstract (arXiv)
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.
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References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
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