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Related Concept Videos

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
Overview of Electron Microscopy01:25

Overview of Electron Microscopy

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.
Overview of Microscopy Techniques01:22

Overview of Microscopy Techniques

The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...

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Updated: May 12, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Picometer-Precision Atomic Position Tracking through Electron Microscopy

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Relating human and AI-based detection limits in scanning electron microscopy dimensional metrology.

Peter Bajcsy1, Pushkar Sathe1, Andras A Vladar1

  • 1National Institute of Standards and Technology, Gaithersburg, Maryland, United States.

Journal of Micro/Nanolithography, MEMS, and MOEMS : JM3
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

This study establishes a method to determine the detection limits of AI-based SEM dimensional metrology. It relates SEM image quality to AI model accuracy, improving trust in critical dimension measurements for semiconductor manufacturing.

Keywords:
artificial intelligence modeldetection limitsdimensional metrologyscanning electron microscopy

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Area of Science:

  • Metrology
  • Artificial Intelligence
  • Semiconductor Manufacturing

Background:

  • Scanning Electron Microscopy (SEM) is crucial for nanoscale measurements in semiconductor manufacturing.
  • Low electron beam current and dose in SEM imaging produce noisy, low-contrast images, hindering traditional analysis.
  • Sensitive integrated circuit (IC) structures require careful handling to prevent charging and damage.

Purpose of the Study:

  • To investigate the detection limits of AI-based SEM image segmentation for IC structure detection.
  • To establish the relationship between SEM image quality and AI model accuracy.
  • To compare AI model detection limits with human detection limits.

Main Methods:

  • Utilized SEM image simulation software to generate datasets with varying noise and contrast.
  • Characterized SEM images using 25 image quality metrics.
  • Trained and evaluated three AI models on these image datasets.
  • Mapped image quality characteristics to AI model accuracy metrics.

Main Results:

  • Established detection limits for AI models based on image quality and confidence levels.
  • Related AI model detection limits to human detection limits, using Rose's signal-to-noise ratio (SNR=5) as a benchmark.
  • Demonstrated upper and lower SNR bounds for three AI models relative to human detection limits.

Conclusions:

  • Developed a method to determine AI-based SEM dimensional metrology detection limits.
  • The findings are relevant for semiconductor vendors and AI model consumers, enhancing trust in critical dimension measurements.
  • The method allows for trusted AI model characterization for semiconductor production using noisy SEM images.