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Page 1 of 2

  • Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

  • Michael Xu, Abinash Kumar, James M. LeBeau
  • Journal: Microscopy and Microanalysis / 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: Microscopy and Microanalysis / 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.

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