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Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy.
- Source :
-
Scientific Reports . 8/22/2023, Vol. 13 Issue 1, p1-10. 10p. - Publication Year :
- 2023
-
Abstract
- Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 170061753
- Full Text :
- https://doi.org/10.1038/s41598-023-40943-7