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StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron

A De Backer1, K H W van den Bos1, W Van den Broek2

  • 1Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium.

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Summary

A new algorithm precisely quantifies atomic column positions and intensities from scanning transmission electron microscopy (STEM) images. This method accurately analyzes atomic structures, even in low-dose images, and is available via the StatSTEM program.

Keywords:
Dataprocessing/image processingGeneral methods in microscopyHigh-resolution (scanning) transmission electron microscopy (HR (S)TEM)Model-based fittingQuantitative electron microscopyStatistical parameter estimation theoryTools

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Area of Science:

  • Materials Science
  • Physics
  • Nanotechnology

Background:

  • Accurate quantification of atomic structures is crucial for understanding material properties at the nanoscale.
  • Traditional methods for analyzing atomic resolution microscopy images often struggle with overlapping atomic columns and low signal-to-noise ratios.

Purpose of the Study:

  • To introduce an efficient model-based estimation algorithm for quantifying atomic column positions and intensities from scanning transmission electron microscopy ((S)TEM) images.
  • To investigate the accuracy and precision of this algorithm for atomic column measurements using annular dark field (ADF) STEM imaging.
  • To develop a user-friendly software tool, StatSTEM, for implementing this quantification method.

Main Methods:

  • Development of a model-based least squares estimation algorithm applied to image segments containing individual atomic columns.
  • The algorithm explicitly accounts for the overlap between neighboring atomic columns to enable large field-of-view analysis.
  • Investigation of measurement accuracy and precision using simulated and experimental ADF STEM data.

Main Results:

  • The algorithm achieves high precision in estimating atomic column positions and scattering cross-sections, even with low-dose images.
  • The model-based approach effectively handles overlapping columns, outperforming methods relying on integrated intensity within Voronoi cells.
  • Demonstrated accurate estimation of inter-column distances as a function of their separation.

Conclusions:

  • The developed model-based algorithm provides a robust and accurate method for quantifying atomic structures from (S)TEM images.
  • StatSTEM offers an accessible, free, and user-friendly implementation of this advanced quantification technique for researchers.
  • This approach enhances the reliability of nanoscale structural analysis, particularly in challenging imaging conditions.