Kavli Affiliate: David A. Muller
| First 5 Authors: Desheng Ma, Steven E. Zeltmann, Chenyu Zhang, Zhaslan Baraissov, Yu-Tsun Shao
| Summary:
Precise alignment of the electron beam is critical for successful application
of scanning transmission electron microscopes (STEM) to understanding materials
at atomic level. Despite the success of aberration correctors, aberration
correction is still a complex process. Here we approach aberration correction
from the perspective of accelerator physics and show it is equivalent to
minimizing the emittance growth of the beam, the span of the phase space
distribution of the probe. We train a deep learning model to predict emittance
growth from experimentally accessible Ronchigrams. Both simulation and
experimental results show the model can capture the emittance variation with
aberration coefficients accurately. We further demonstrate the model can act as
a fast-executing function for the global optimization of the lens parameters.
Our approach enables new ways to quickly quantify and automate aberration
correction that takes advantage of the rapid measurements possible with
high-speed electron cameras. In part II of the paper, we demonstrate how the
emittance metric enables rapid online tuning of the aberration corrector using
Bayesian optimization.
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