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 |
Published: |
MDPI AG
2023-08-01
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Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/11/15/3428 |