Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks

Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks

This paper concerns the development of a machine learning tool to detect anomalies in the molecular structure of Gallium Arsenide. We employ a combination of a CNN and a PCA reconstruction to create the model, using real images taken with an electron microscope in training and testing. The methodolo...

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Bibliographic Details
Main Authors: Timothy Roche, Aihua Wood, Philip Cho, Chancellor Johnstone
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Mathematics
Subjects:
electron microscope
convolutional neural networks (CNNs)
anomaly detection
principal component analysis (PCA)
machine learning
deep learning
Online Access:https://www.mdpi.com/2227-7390/11/15/3428

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