Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks
Document Type
Article
Publication Date
8-7-2023
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.
DOI
10.3390/math11153428
Source Publication
Mathematics
Recommended Citation
Roche, T., Wood, A., Cho, P., & Johnstone, C. (2023). Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks. Mathematics, 11(15), 3428. https://doi.org/10.3390/math11153428
Comments
© 2023 by the authors. Licensee MDPI, Basel, Switzerland.
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[*] Author notes: T. Roche and P. Cho were in AFIT graduate programs at the time of this effort.