Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks
Name
mathematics-11-03428.pdf
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
a9281bf187fc749f92b76938a6104c77
Author(s)
Roche, Timothy
Wood, Aihua
Cho, Philip
Johnstone, Chancellor
Date Issued
August 7, 2023
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Mathematics 11 (15): 3428 (2023)
Version
Final published version
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.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/math11153428
