Automating STEM Aberration Correction via Bayesian Optimization

Journal Article · Sat Jul 22 00:00:00 EDT 2023 · Microscopy and Microanalysis

DOI:https://doi.org/10.1093/micmic/ozad067.971· OSTI ID:2234070

Pattison, Alexander J. ^[1]; Noack, Marcus ^[2]; Ercius, Peter ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Multipole aberration correctors have given scanning transmission electron microscopes (STEM) the ability to produce high-quality, atomic-resolution images, enabling STEM to be a key tool in material sciences for characterizing the structure and composition of materials. However, the process of correcting these aberrations typically requires human input and is accomplished using scanned STEM or Ronchigram images at different focii and beam tilts. In this work, we demonstrate an automated on-sample aberration correction system using Bayesian Optimization. We have developed a Python-based server able to communicate with the CEOS DCOR aberration corrector and the Thermo Fischer microscope scripting interface. This server allows us to change aberrations, acquire images and perform basic image analysis.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2234070
• Journal Information: Microscopy and Microanalysis, Journal Name: Microscopy and Microanalysis Journal Issue: Supplement_1 Vol. 29; ISSN 1431-9276
• Publisher: Microscopy Society of America (MSA)
• Country of Publication: United States
• Language: English

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