Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Yan Zhang, G. M.Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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. 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. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

Original languageEnglish (US)
Pages (from-to)90-97
Number of pages8
StatePublished - Jan 2018


  • Dose reduction
  • Dynamic sampling
  • Energy dispersive spectroscopy (EDS)
  • Neural networks
  • Scanning electron microscopy (SEM)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling'. Together they form a unique fingerprint.

Cite this