Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
This work addresses the need for faster, less damaging elemental mapping in materials science, particularly for biological specimens and polymers, though it appears incremental as it builds on existing dynamic sampling and neural network techniques.
The paper tackles the problem of irradiation damage and long acquisition times in energy-dispersive spectroscopy (EDS) for beam-sensitive materials by introducing a machine learning-based dynamic sparse sampling method, achieving up to a 90% reduction in total sampling while maintaining data fidelity.
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.