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Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
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Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
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Journal Article
Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
2023
Overview
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 WSe
2
film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.
Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
