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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Timothy Roche & Aihua Wood & Philip Cho & Chancellor Johnstone, 2023. "Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-10, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3428-:d:1211870
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    References listed on IDEAS

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    1. Philip Cho & Aihua Wood & Krishnamurthy Mahalingam & Kurt Eyink, 2021. "Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning," Mathematics, MDPI, vol. 9(11), pages 1-16, May.
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