Morphological Analysis of Semiconductor Microstructures using Skeleton Graphs

2025-08-11

AI Summary

This study investigates the relative influence of ion beam irradiation angle versus fluence on microstructural morphology evolution of germanium (Ge) surfaces. We propose a quantitative analytical framework integrating skeleton-graph topological characterization with graph convolutional network (GCN) embedding, coupled with principal component analysis (PCA) and the Davies–Bouldin index to evaluate class separability of microstructures across irradiation conditions. Results demonstrate that irradiation angle is the dominant parameter governing surface morphological evolution—its effect substantially outweighs that of fluence. The framework enables unsupervised, interpretable discrimination of microstructural patterns. To our knowledge, this work represents the first application of graph neural networks to ion-beam irradiation-induced morphology analysis. It establishes a novel paradigm for precise microstructural engineering of semiconductor materials and provides a generalizable computational toolkit for quantitative microstructure–processing–property analysis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Analyze semiconductor microstructure morphology using skeleton graphs
• Evaluate irradiation angle impact on Ge surface properties
• Compare cluster separability with Davies-Bouldin index

Innovation

• Extract topological features as skeleton graphs
• Embed graphs using convolutional network
• Analyze embeddings with principal component analysis