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...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 firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.展开更多
High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated la...High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated labeling removes the time-consuming manual labeling of training data,but introduces label error,and subsequently classification error in the trained neural network.Here,we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles.Using the mirror relationship between images of opposite handed particles,we artificially create populations of varying label error.We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset.Of the three training methods considered,we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets,where size and other morphological variables are held constant,but that a co-teaching approach performs the best in practical application.展开更多
基金funded by the US Department of Energy in the program "Electron Distillery 2.0: Massive Electron Microscopy Data to Useful Information with AI/ML."Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231+1 种基金This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award BES-ERCAP0028035S.M.R. and C.O. acknowledge support from the U.S. Department of Energy Early Career Research Award program. J.P. acknowledges financial support from the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (Grant No. RS-2023-00283902, and RS-202400408823). We gratefully acknowledge CEOS, GmbH for providing the server enabling communication with the corrector and I. Massmann for technical assistance.
文摘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 firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.
基金Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the US Department of Energy under Contract No.DE-AC02-05CH11231This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.DGE-1752814This work was also supported by National Science Foundation STROBE grant DMR-1548924。
文摘High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated labeling removes the time-consuming manual labeling of training data,but introduces label error,and subsequently classification error in the trained neural network.Here,we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles.Using the mirror relationship between images of opposite handed particles,we artificially create populations of varying label error.We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset.Of the three training methods considered,we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets,where size and other morphological variables are held constant,but that a co-teaching approach performs the best in practical application.
