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  • Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

  • Michael Xu, Abinash Kumar, James M. LeBeau
  • Journal:
    / Volume 28 / Issue 6 / December 2022
    Published online by Cambridge University Press:
    05 September 2022, pp. 1952-1960
    Print publication:
    December 2022

  • Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.

  • Expanding the Dimensions of a Small, Two-Dimensional Diffraction Detector

  • Xi Chen, Matthew R. Hauwiller, Abinash Kumar, Aubrey N. Penn, James M. LeBeau
  • Journal:
    / Volume 26 / Issue 5 / October 2020
    Published online by Cambridge University Press:
    11 August 2020, pp. 938-943
    Print publication:
    October 2020

  • We report an approach to expand the effective number of pixels available to small, two-dimensional electron detectors. To do so, we acquire subsections of a diffraction pattern that are then accurately stitched together in post-processing. Using an electron microscopy pixel array detector (EMPAD) that has only 128 × 128 pixels, we show that the field of view can be expanded while achieving high reciprocal-space sampling. Further, we highlight the need to properly account for the detector position (rotation) and the non-orthonormal diffraction shift axes to achieve an accurate reconstruction. Applying the method, we provide examples of spot and convergent beam diffraction patterns acquired with a pixelated detector.

  • In Situ Ferroelectric Domain Dynamics Probed with Differential Phase Contrast Imaging

  • Abinash Kumar, Rohan Dhall, James M. LeBeau
  • Journal:
    / Volume 25 / Issue S2 / August 2019
    Published online by Cambridge University Press:
    05 August 2019, pp. 1838-1839
    Print publication:
    August 2019
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  • Standardless EDS Composition Analysis using Quantitative Annular Dark-Field Imaging

  • J. Houston Dycus, Weizong Xu, James M. LeBeau
  • Journal:
    / Volume 23 / Issue S1 / July 2017
    Published online by Cambridge University Press:
    04 August 2017, pp. 512-513
    Print publication:
    July 2017
    • Article
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  • Numerical Modeling of Specimen Geometry for Quantitative Multiple Detector EDS

  • Weizong Xu, J. Houston Dycus, James M. LeBeau
  • Journal:
    / Volume 23 / Issue S1 / July 2017
    Published online by Cambridge University Press:
    04 August 2017, pp. 390-391
    Print publication:
    July 2017
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Page 1 of 2