扫描电子显微镜中基于自相关的束形优化

中文摘要 AI 辅助

现有许多扫描电子显微镜的自动聚焦算法，通常仅优化单一衍生指标（如图像清晰度），未考虑固有束形。本文提出一种基于采集图像自相关的扫描电子显微镜自动聚焦与像散校正算法：第一部分建立用自相关近似当前束形的理论基础；第二部分利用该结果对不同表面的束形进行约束，采用贝叶斯优化调节工作距离，尤其优化像散。通过对比优化前后自相关阵列的半高全宽，以及优化前后样品表面图像的视觉对比，证明该算法可成功优化扫描参数，能在不同放大倍数下有效聚焦于多种形貌。

英文摘要

Many automatically focusing algorithms for scanning electron microscopes exist. Typically, these optimize a single derived metric (such as image sharpness) without considering the intrinsic beam shape. We describe an algorithm to automatically focus and stigmate scanning electron microscopes based on the autocorrelation of captured images. In the first part of this work, we develop the theoretical basis of using the autocorrelation to approximate the current beam shape. In the second part, we use this to condition the beam shape on various surfaces, using Bayesian optimization to tune both working distance and, in particular, stigmation. Through a comparison of the full width at half maximum of the autocorrelation arrays before and after optimization and a visual comparison of images taken of the sample surface before and after optimization, we show that the algorithm is able to successfully optimize the scanning parameters. Overall, our algorithm can effectively focus on a variety of topographies at different magnifications.

发表机构

• University of Missouri（密苏里大学）
• Cornell University（康奈尔大学）

机构由 AI 辅助整理，请以论文原文为准。