Kavli Affiliate: David A. Muller
| First 5 Authors: Desheng Ma, Steven E. Zeltmann, Chenyu Zhang, Zhaslan Baraissov, Yu-Tsun Shao
| Summary:
Aberration-corrected Scanning Transmission Electron Microscopy (STEM) has
become an essential tool in understanding materials at the atomic scale.
However, tuning the aberration corrector to produce a sub-{AA}ngstr"om probe
is a complex and time-costly procedure, largely due to the difficulty of
precisely measuring the optical state of the system. When measurements are both
costly and noisy, Bayesian methods provide rapid and efficient optimization. To
this end, we develop a Bayesian approach to fully automate the process by
minimizing a new quality metric, beam emittance, which is shown to be
equivalent to performing aberration correction. In part I, we derived several
important properties of the beam emittance metric and trained a deep neural
network to predict beam emittance growth from a single Ronchigram. Here we use
this as the black box function for Bayesian Optimization and demonstrate
automated tuning of simulated and real electron microscopes. We explore
different surrogate functions for the Bayesian optimizer and implement a deep
neural network kernel to effectively learn the interactions between different
control channels without the need to explicitly measure a full set of
aberration coefficients. Both simulation and experimental results show the
proposed method outperforms conventional approaches by achieving a better
optical state with a higher convergence rate.
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