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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/15/3428 |
| _version_ | 1797586388790542336 |
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| author | Timothy Roche Aihua Wood Philip Cho Chancellor Johnstone |
| author_facet | Timothy Roche Aihua Wood Philip Cho Chancellor Johnstone |
| author_sort | Timothy Roche |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:22:29Z |
| format | Article |
| id | doaj.art-72adde813f84481ea6c5915ae5ec44e9 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:22:29Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-72adde813f84481ea6c5915ae5ec44e92023-11-18T23:16:32ZengMDPI AGMathematics2227-73902023-08-011115342810.3390/math11153428Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural NetworksTimothy Roche0Aihua Wood1Philip Cho2Chancellor Johnstone3Department of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USADepartment of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USADepartment of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USADepartment of Mathematics & Statistics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USAThis 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.https://www.mdpi.com/2227-7390/11/15/3428electron microscopeconvolutional neural networks (CNNs)anomaly detectionprincipal component analysis (PCA)machine learningdeep learning |
| spellingShingle | Timothy Roche Aihua Wood Philip Cho Chancellor Johnstone Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks Mathematics electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) machine learning deep learning |
| title | Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks |
| title_full | Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks |
| title_fullStr | Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks |
| title_full_unstemmed | Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks |
| title_short | Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks |
| title_sort | anomaly detection in the molecular structure of gallium arsenide using convolutional neural networks |
| topic | electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) machine learning deep learning |
| url | https://www.mdpi.com/2227-7390/11/15/3428 |
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