Title: Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling,
Abstract: 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.