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October 9, 2025Open Access

Neural Object Detection for 4D STEM: High-Throughput Sub-Pixel Electron Diffraction Pattern Recognition

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Authors

AGArda GençRSR. Silverstein

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Overview

An end-to-end framework enhances high-throughput analysis in electron diffraction patterns, suggesting improved workflows.

Key Points

  • Strain measurement precision of 5x10^-4 was achieved, highlighting the method's effectiveness in material research.
  • The asynchronous workflow enables speeds over 100 frames per second, significantly improving crystallographic phase identification.
  • Utilizing a neural network-based model allows for sub-pixel accurate localization of electron diffraction patterns.
  • This approach addresses challenges in conventional transmission electron microscopy data processing, reducing manual intervention.

Cite This Study

Genç et al. (2025) studied this question.

synapsesocial.com/papers/68e8439a9989581a2fd4e1b5https://doi.org/10.48550/arxiv.2506.04477
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