发表机构
Seoul National University; University of Southern California（首尔国立大学; 南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过贝叶斯优化联合估计多层电子叠层衍射中的厚度参数，从4D-STEM数据同时获得局部厚度与FIB损伤深度分布，实现自洽的定量分析。
AI 中文摘要
定量电子显微镜需要准确了解样品厚度，因为动力学散射强烈影响图像衬度和衍射强度。在多层电子叠层衍射（MEP）中，样品厚度是其前向模型所需的输入参数，然而透射电子显微镜（TEM）薄片的局部厚度往往正是未知的。此外，厚度与探针离焦量及切片数量相互耦合，使得从重建结果本身确定厚度变得非平凡。在此，我们评估了这些参数的联合贝叶斯优化能否从四维扫描透射电子显微镜（4D-STEM）数据中提供具有物理意义的局部厚度估计。应用于楔形硅薄片时，该优化重现了与电子能量损失谱（EELS）测量的相似局部厚度变化，相对于EELS估计存在10-17%的系统性偏差。利用优化后的重建参数，深度分辨MEP进一步在同一重建体积内将晶态硅内部与聚焦离子束（FIB）诱导的非晶表面层分离开来。在最终铣削电压为2、5、8和30 kV的区域，所得非晶层厚度分别为3.2、5.0、7.0和26.4 nm，随铣削电压单调增加，并与先前的横截面测量结果合理一致。这些结果表明，将MEP与贝叶斯优化应用于单个4D-STEM数据集，可同时提供局部厚度估计和FIB诱导损伤的深度分辨表征，为更自洽、厚度感知的定量4D-STEM分析提供了一条途径。
英文摘要
Quantitative electron microscopy requires accurate knowledge of specimen thickness because dynamical scattering strongly affects image contrast and diffraction intensities. In multislice electron ptychography (MEP), specimen thickness is a required input parameter to its forward model, yet the local thickness of a transmission electron microscope (TEM) lamella is often precisely what is unknown. Moreover, thickness is coupled to probe defocus and the number of slices, making its determination from the reconstruction itself nontrivial. Here, we assess whether joint Bayesian optimization of these parameters can provide a physically meaningful estimate of local thickness from four-dimensional scanning transmission electron microscopy (4D-STEM) data. Applied to a wedge-shaped silicon lamella, the optimization reproduced similar local thickness variation measured by electron energy loss spectroscopy (EELS), with a systematic offset of 10-17% relative to the EELS estimates. Using the optimized reconstruction parameters, depth-resolved MEP further separated the crystalline silicon interior from focused ion beam (FIB)-induced amorphous surface layers within the same reconstructed volume. Regions milled at final voltages of 2, 5, 8, and 30 kV yielded amorphous-layer thicknesses of 3.2, 5.0, 7.0, and 26.4 nm, respectively, increasing monotonically with milling voltage and agreeing reasonably with previous cross-sectional measurements. These results show that applying MEP coupled with Bayesian optimization on a single 4D-STEM dataset can provide both local thickness estimates and depth-resolved characterization of FIB-induced damage, offering a route toward more self-consistent, thickness-aware quantitative 4D-STEM analysis.
Comments39 pages, 5 figures, 10 supplementary figures, 3 supplementary tables
