Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

Philip Cho, Aihua Wood, Krishnamurthy Mahalingam and Kurt Eyink
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Philip Cho: Air Force Institute of Technology, Department of Mathematics & Statistics, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA
Aihua Wood: Air Force Institute of Technology, Department of Mathematics & Statistics, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA
Krishnamurthy Mahalingam: Air Force Research Lab, Material and Manufacturing Directorate, Wright-Patterson AFB, OH 45433, USA
Kurt Eyink: Air Force Research Lab, Material and Manufacturing Directorate, Wright-Patterson AFB, OH 45433, USA

Mathematics, 2021, vol. 9, issue 11, 1-16

Abstract: Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via simulation. Motivated by a desire for machine learning methods that can be trained on experimental data, we propose two self-supervised machine learning algorithms that are trained solely on images that are defect-free. Our proposed methods use principal components analysis (PCA) and convolutional neural networks (CNN) to analyze a TEM image and predict the location of a defect. Using simulated TEM images, we show that PCA can be used to accurately locate point defects in the case where there is no imaging noise. In the case where there is imaging noise, we show that incorporating a CNN dramatically improves model performance. Our models rely on a novel approach that uses the residual between a TEM image and its PCA reconstruction.

Keywords: transmission electron microscopy (TEM); convolutional neural networks (CNN); anomaly detection; principal component analysis (PCA); machine learning; deep learning; neural networks; Gallium-Arsenide (GaAs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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