Physics-informed Bayesian Optimization of an Electron Microscope

Precise control of the electron beam shape is critical for the successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. However, the nature of magnetic lenses introduces various orders of aberrations and makes aberration corrector tuning a complex and time-consuming procedure. Here we approach the problem from the perspective of accelerator physics and demonstrate the equivalence between aberration correction and beam emittance minimization in phase space. We show a deep neural network can accurately capture phase space variations from electron Ronchigrams, enabling a rapidly-executing beam quality measurement tool. A Bayesian approach is adopted to optimize for the system for minimum emittance growth and provides the full posterior of the response over control parameters to account for uncertainties at each query. Furthermore, a deep kernel is implemented and shown to effectively learn the correlations between input dimensions, which can generalize to other accelerator tuning tasks as well. Both simulation and experimental results show the proposed method outperforms existing alignment approaches. This new scheme enables fully automated aberration corrector tuning, achieving greater speed and less human bias.