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: | , , , |
|---|---|
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2023
|
| Online Access: | https://hdl.handle.net/1721.1/152072 |