通过优化倾斜角度减轻反射断层扫描中倾斜引起的伪影

Sander Senhorst, Yifeng Shao, Sven Weerdenburg, Roland Horsten, Christina Porter, Wim Coene
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引用次数: 0

摘要

在厚基底表面对三维纳米结构进行非破坏性成像时,反射几何中的层析成像技术大有可为。在近光条件下获得高质量反射-层析成像的主要挑战是校准入射角,以便在称为 "倾斜平面校正"(TPC)的过程中校正测量到的弯曲衍射图样。在这项工作中,我们利用基于自动微分(AD)建模的灵活性实现了另一种方法,即在前向模型中包含倾斜传播。利用 AD,我们可以联合优化典型探头和目标的倾斜角,从而无需进行精确校准或随机搜索优化。我们使用基于台式高次谐波发生(HHG)源或可见光激光器的极紫外(EUV)光束线生成的数据集对该方法进行了验证。我们证明,对于入射角$70deg$的探针光束,所提出的方法可以收敛到$\pm 0.05\deg$的精度,其精度可能足以用作校准方法。此外,我们还证明了对倾斜角的优化可以减少伪影并提高重建保真度。AD 的使用不仅简化了当前的双层图像重建过程,而且还能优化其他领域中更复杂的模型,这对未来计算成像的发展无疑是至关重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mitigating tilt-induced artifacts in reflection ptychography via optimization of the tilt angles
Ptychography in a reflection geometry shows great promise for non-destructive imaging of 3-dimensional nanostructures at the surface of a thick substrate. A major challenge to obtain high quality reflection-ptychographic images under near-grazing conditions has been to calibrate the incidence angle used to straighten the measured curved diffraction patterns in a process referred to as 'tilted plane correction' (TPC). In this work, we leverage the flexibility of automatic differentiation (AD)-based modeling to realise an alternative approach, where the tilted propagation is included into the forward model. Use of AD allows us to jointly optimize the tilt angles with the typical probe and object, eliminating the need for accurate calibration or random search optimization. The approach was validated using datasets generated with an extreme ultraviolet (EUV) beamline based on either a tabletop high harmonic generation (HHG) source or a visible laser. We demonstrate that the proposed approach can converge to a precision of $\pm 0.05\deg$ for probe beams at $70\deg$ angle of incidence, possibly precise enough for use as a calibration approach. Furthermore, we demonstrate that optimizing for the tilt angles reduces artifacts and increases reconstruction fidelity. Use of AD not only streamlines the current ptychographic reconstruction process, but should also enable optimization of more complex models in other domains, which will undoubtedly be essential for future advancements in computational imaging.
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