Ma Desheng, Zeltmann Steven E, Zhang Chenyu, Baraissov Zhaslan, Shao Yu-Tsun, Duncan Cameron, Maxson Jared, Edelen Auralee, Muller David A
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.
Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials and School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.
Ultramicroscopy. 2025 Jul;273:114137. doi: 10.1016/j.ultramic.2025.114137. Epub 2025 Apr 5.
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.
电子束的精确对准对于扫描透射电子显微镜（STEM）成功应用于原子级材料理解至关重要。尽管像差校正器取得了成功，但像差校正仍然是一个复杂的过程。在这里，我们从加速器物理的角度探讨像差校正，并表明它等同于最小化束流的发射度增长，即探针相空间分布的跨度。我们训练了一个深度学习模型，从实验可获取的龙奇图预测发射度增长。模拟和实验结果均表明，该模型能够准确捕捉发射度随像差系数的变化。我们进一步证明，该模型可作为透镜参数全局优化的快速执行函数。我们的方法为快速量化和自动化像差校正提供了新途径，利用高速电子相机实现快速测量。在本文的第二部分，我们展示了发射度度量如何使用贝叶斯优化实现像差校正器的快速在线调谐。
