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BEACON -- Automated Aberration Correction for Scanning Transmission Electron Microscopy using Bayesian Optimization
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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.
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Emittance Minimization for Aberration Correction II: Physics-informed Bayesian Optimization of an Electron Microscope
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.
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