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

Timothy Roche (), Aihua Wood (), Philip Cho and Chancellor Johnstone
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Timothy Roche: Department of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA
Aihua Wood: Department of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA
Philip Cho: Department of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA
Chancellor Johnstone: Department of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA

Mathematics, 2023, vol. 11, issue 15, 1-10

Abstract: 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 methodology developed allows for the creation of a defect detection model, without any labeled images of defects being required for training. The model performed well on all tests under the established assumptions, allowing for reliable anomaly detection. To the best of our knowledge, such methods are not currently available in the open literature; thus, this work fills a gap in current capabilities.

Keywords: electron microscope; convolutional neural networks (CNNs); anomaly detection; principal component analysis (PCA); machine learning; deep learning; neural networks; Gallium Arsenide (GaAs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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