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Partial Scanning Transmission Electron Microscopy with Deep Learning.

Jeffrey M Ede1, Richard Beanland2

  • 1University of Warwick, Department of Physics, Coventry, CV4 7AL, UK. j.m.ede@warwick.ac.uk.

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Summary
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Deep learning enhances electron microscopy by using generative adversarial networks to reconstruct detailed images from partial scans, significantly reducing scan time and electron beam exposure.

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Area of Science:

  • Materials Science
  • Electron Microscopy
  • Artificial Intelligence

Background:

  • Compressed sensing reduces data acquisition time and electron beam exposure in electron microscopy.
  • Deep learning has shown promise in improving compressed sensing techniques.

Purpose of the Study:

  • To develop a novel deep learning model for reconstructing high-resolution scanning transmission electron micrographs from partial scans.
  • To significantly decrease electron microscope scan time and beam exposure while preserving image quality.

Main Methods:

  • Development of a two-stage, multiscale generative adversarial neural network (GAN).
  • Training GANs on a dataset of 16,227 scanning transmission electron micrographs with partial scans (spiral, jittered gridlike).
  • Implementation of adaptive learning rate clipping and an auxiliary trainer network for performance optimization.

Main Results:

  • Achieved significant reduction in electron beam coverage: 17.9x with 3.8% error for spiral scans and 87.0x with 6.2% error using MSE pre-training.
  • Generated realistic 512x512 scanning transmission electron micrographs from incomplete data.

Conclusions:

  • The developed deep learning approach effectively reconstructs high-quality electron microscopy images from undersampled data.
  • This method offers a substantial reduction in scan time and electron beam exposure, crucial for sensitive materials analysis.