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Related Concept Videos

Overview of Electron Microscopy01:25

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The wavelengths of visible light ultimately limit the maximum theoretical resolution of images created by light microscopes. Most light microscopes can only magnify 1000X, and a few can magnify up to 1500X. Electrons, like electromagnetic radiation, can behave like waves, but with wavelengths of 0.005 nm, they produce significantly greater resolution up to 0.05 nm as compared to 500 nm for visible light. An electron microscope (EM) can create a sharp image that is magnified up to 2,000,000X.
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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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Related Experiment Video

Updated: May 14, 2025

Single-Digit Nanometer Electron-Beam Lithography with an Aberration-Corrected Scanning Transmission Electron Microscope
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Emittance minimization for aberration correction II: Physics-informed Bayesian optimization of an electron

Desheng Ma1, Steven E Zeltmann2, Chenyu Zhang1

  • 1School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.

Ultramicroscopy
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

We developed a Bayesian approach to automate aberration correction in Scanning Transmission Electron Microscopy (STEM). This method uses beam emittance and a deep neural network for faster, more accurate atomic-scale material analysis.

Keywords:
Aberration correctionBayesian optimizationBeam emittanceMachine learningTransmission electron microscopy

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Area of Science:

  • Materials Science
  • Physics
  • Electron Microscopy

Background:

  • Aberration-corrected Scanning Transmission Electron Microscopy (STEM) is crucial for atomic-scale material analysis.
  • Manual tuning of aberration correctors is complex, time-consuming, and prone to noise.
  • Accurate measurement of the electron microscope's optical state is challenging.

Purpose of the Study:

  • To develop a fully automated Bayesian approach for aberration correction in STEM.
  • To minimize beam emittance as a quality metric equivalent to aberration correction.
  • To improve the speed and accuracy of achieving a sub-Ångström probe.

Main Methods:

  • Utilized a Bayesian optimization framework with beam emittance as the objective function.
  • Employed a deep neural network to predict beam emittance growth from Ronchigram images.
  • Explored various surrogate functions and implemented a deep neural network kernel for optimization.

Main Results:

  • Demonstrated automated tuning of both simulated and real electron microscopes.
  • The Bayesian approach achieved a higher convergence rate compared to conventional methods.
  • The method successfully optimized the optical state, leading to improved probe quality.

Conclusions:

  • The developed Bayesian method effectively automates STEM aberration correction.
  • Minimizing beam emittance provides an efficient and accurate quality metric.
  • This approach significantly outperforms conventional tuning methods in speed and precision.