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Title: Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. Here, in this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. In conclusion, we believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division
  2. Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division ; Purdue Univ., West Lafayette, IN (United States). ECE Department
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
  4. Northwestern Univ., Evanston, IL (United States). Department of Materials Science and Engineering
  5. Purdue Univ., West Lafayette, IN (United States). ECE Department
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Ultramicroscopy
Additional Journal Information:
Journal Volume: 184; Journal Issue: PB; Journal ID: ISSN 0304-3991
Publisher:
Elsevier
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); Argonne National Laboratory - Laboratory Directed Research and Development (LDRD)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 47 OTHER INSTRUMENTATION; Energy dispersive spectroscopy (EDS); Neural Networks; SLADS; dose reduction; dynamic sampling; scanning electron microscopy (SEM)
OSTI Identifier:
1416973

Zhang, Yan, Godaliyadda, G. M. Dilshan, Ferrier, Nicola, Gulsoy, Emine B., Bouman, Charles A., and Phatak, Charudatta. Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling. United States: N. p., Web. doi:10.1016/j.ultramic.2017.10.015.
Zhang, Yan, Godaliyadda, G. M. Dilshan, Ferrier, Nicola, Gulsoy, Emine B., Bouman, Charles A., & Phatak, Charudatta. Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling. United States. doi:10.1016/j.ultramic.2017.10.015.
Zhang, Yan, Godaliyadda, G. M. Dilshan, Ferrier, Nicola, Gulsoy, Emine B., Bouman, Charles A., and Phatak, Charudatta. 2017. "Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling". United States. doi:10.1016/j.ultramic.2017.10.015. https://www.osti.gov/servlets/purl/1416973.
@article{osti_1416973,
title = {Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling},
author = {Zhang, Yan and Godaliyadda, G. M. Dilshan and Ferrier, Nicola and Gulsoy, Emine B. and Bouman, Charles A. and Phatak, Charudatta},
abstractNote = {Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. Here, in this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. In conclusion, we believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.},
doi = {10.1016/j.ultramic.2017.10.015},
journal = {Ultramicroscopy},
number = PB,
volume = 184,
place = {United States},
year = {2017},
month = {10}
}