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Resolution enhancement in scanning electron microscopy using deep learning.

Kevin de Haan1,2,3, Zachary S Ballard1,2,3, Yair Rivenson4,5,6

  • 1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

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We developed a deep learning method to enhance scanning electron microscopy (SEM) image resolution. This technique improves image quality, reduces imaging time, and minimizes sample damage.

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Area of Science:

  • Materials Science
  • Microscopy
  • Artificial Intelligence

Background:

  • Scanning electron microscopy (SEM) is crucial for high-resolution imaging.
  • Achieving higher resolutions often requires longer imaging times and can cause sample damage.
  • Current methods for resolution enhancement are limited.

Purpose of the Study:

  • To introduce a deep learning-based super-resolution technique for SEM images.
  • To validate the accuracy of the deep learning method by comparing inferred features with high-resolution SEM images.
  • To assess the impact of the method on image quality and imaging parameters.

Main Methods:

  • Utilized a generative adversarial network (GAN) for image super-resolution.
  • Employed co-registered low-resolution and high-resolution SEM images for training and validation.
  • Performed spatial frequency analysis to compare image spectra.

Main Results:

  • The deep learning model successfully inferred unresolved features in low-resolution SEM images.
  • Generated images exhibited frequency spectra comparable to authentic high-resolution SEM images.
  • The technique allows for faster image acquisition and reduces electron charging and sample damage.

Conclusions:

  • Generative adversarial networks offer a viable approach for SEM image resolution enhancement.
  • This deep learning method improves SEM imaging efficiency and sample preservation.
  • The technique holds potential for broader applications in microscopy and materials analysis.