Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling

TL;DR

This paper introduces a machine learning-based dynamic sampling method for energy-dispersive spectroscopy that significantly reduces electron exposure and data acquisition time, enabling analysis of beam-sensitive materials with minimal damage.

Contribution

The work presents a novel supervised learning approach for dynamic sparse sampling in EDS, achieving up to 90% reduction in data collection while preserving data quality.

Findings

Reduced sampling by up to 90% with maintained data fidelity

Enabled analysis of beam-sensitive materials previously inaccessible

Improved efficiency in spectroscopic imaging

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.