Pith. sign in

REVIEW 1 cited by

BEACON -- Automated Aberration Correction for Scanning Transmission Electron Microscopy using Bayesian Optimization

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.14873 v1 pith:AMN2YW5W submitted 2024-10-18 cond-mat.mtrl-sci physics.ins-det

classification cond-mat.mtrl-sciphysics.ins-det
keywords aberrationaberrationsbeaconfirst-second-orderalignmentautomatedbayesian
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order aberrations called BEACON which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings. We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano- and atomic-scale experiments. BEACON can correct all first- and second-order aberrations simultaneously to achieve an initial alignment and first- and second-order aberrations independently for fine alignment. Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Emittance Minimization for Aberration Correction II: Physics-informed Bayesian Optimization of an Electron Microscope

    physics.ins-det 2024-12 conditional novelty 6.0 of 10

    Minimizing a neural-network-estimated beam emittance with Bayesian optimization tunes electron microscope aberrations in minutes, but the real-microscope comparison is scored by the same network.

Pith tools