Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Saugat Kandel (), Tao Zhou, Anakha V. Babu, Zichao Di, Xinxin Li, Xuedan Ma, Martin Holt, Antonino Miceli, Charudatta Phatak and Mathew J. Cherukara ()
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Saugat Kandel: Argonne National Laboratory
Tao Zhou: Argonne National Laboratory
Anakha V. Babu: KLA Corporation
Zichao Di: Argonne National Laboratory
Xinxin Li: Argonne National Laboratory
Xuedan Ma: Argonne National Laboratory
Martin Holt: Argonne National Laboratory
Antonino Miceli: Argonne National Laboratory
Charudatta Phatak: Argonne National Laboratory
Mathew J. Cherukara: Argonne National Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe2 film. Our studies show that a FAST scan of

Date: 2023
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DOI: 10.1038/s41467-023-40339-1

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