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Optimizing Structure Factors From Four-dimensional Scanning Transmission Electron Microscopy via the Scattering

Kousuke Ooe1,2, Alireza Sadri1, Scott D Findlay1

  • 1School of Physics and Astronomy, Monash University, Wellington Rd, Clayton, Victoria 3800, Australia.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm for quantitative potential retrieval in scanning transmission electron microscopy (STEM). This method accurately reconstructs material properties from thicker samples, overcoming multiple scattering challenges.

Keywords:
4D-STEMOBF STEMscanning transmission electron microscopyscattering matrix

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Electron Microscopy

Background:

  • Quantitative potential retrieval in scanning transmission electron microscopy (STEM) is crucial for material characterization.
  • Thicker samples present challenges due to significant multiple scattering effects.

Purpose of the Study:

  • To develop a novel gradient-descent-based optimization algorithm for quantitative potential retrieval from thick samples in 4D STEM.
  • To overcome limitations imposed by multiple scattering in electron microscopy.

Main Methods:

  • Utilized a scattering matrix (S-matrix) to model multiple scattering effects.
  • Employed a gradient-descent optimization algorithm without common regularization terms.
  • Used optimum bright-field STEM data as an initial potential guess and a loss function based on measured/estimated 4D STEM data.

Main Results:

  • Successfully retrieved quantitative potential from thick samples across various simulated conditions (thickness, dose, incoherence).
  • Demonstrated the algorithm's efficacy on experimental 4D STEM data.
  • Investigated the extension of the algorithm for segmented detectors.

Conclusions:

  • The proposed gradient-descent optimization algorithm effectively enables quantitative potential retrieval in STEM for thicker samples.
  • This method advances material characterization capabilities by mitigating multiple scattering artifacts.