Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs

TL;DR

This paper presents a method for analyzing semiconductor microstructures by converting electron microscopy images into skeleton graphs, embedding them with graph convolutional networks, and evaluating morphological variations.

Contribution

It introduces a novel approach combining skeleton graph extraction, graph embedding, and PCA analysis to study microstructure morphology.

Findings

Irradiation angle significantly affects surface morphology.

Graph convolutional embeddings effectively capture topological features.

PCA and Davies-Bouldin index reveal key morphological differences.

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.