Andrle Anna, Hönicke Philipp, Gwalt Grzegorz, Schneider Philipp-Immanuel, Kayser Yves, Siewert Frank, Soltwisch Victor
Physikalisch-Technische Bundesanstalt (PTB), Abbestr. 2-12, 10587 Berlin, Germany.
Helmholtz Zentrum Berlin für Materialien und Energie (HZB), Department Optics and Beamlines, Albert-Einstein-Str. 15, 12489 Berlin, Germany.
Nanomaterials (Basel). 2021 Jun 23;11(7):1647. doi: 10.3390/nano11071647.
The characterization of nanostructured surfaces with sensitivity in the sub-nm range is of high importance for the development of current and next-generation integrated electronic circuits. Modern transistor architectures for, e.g., FinFETs are realized by lithographic fabrication of complex, well-ordered nanostructures. Recently, a novel characterization technique based on X-ray fluorescence measurements in grazing incidence geometry was proposed for such applications. This technique uses the X-ray standing wave field, arising from an interference between incident and the reflected radiation, as a nanoscale sensor for the dimensional and compositional parameters of the nanostructure. The element sensitivity of the X-ray fluorescence technique allows for a reconstruction of the spatial element distribution using a finite element method. Due to a high computational time, intelligent optimization methods employing machine learning algorithms are essential for timely provision of results. Here, a sampling of the probability distributions by Bayesian optimization is not only fast, but it also provides an initial estimate of the parameter uncertainties and sensitivities. The high sensitivity of the method requires a precise knowledge of the material parameters in the modeling of the dimensional shape provided that some physical properties of the material are known or determined beforehand. The unknown optical constants were extracted from an unstructured but otherwise identical layer system by means of soft X-ray reflectometry. The spatial distribution profiles of the different elements contained in the grating structure were compared to scanning electron and atomic force microscopy and the influence of carbon surface contamination on the modeling results were discussed. This novel approach enables the element sensitive and destruction-free characterization of nanostructures made of silicon nitride and silicon oxide with sub-nm resolution.
对亚纳米范围内具有灵敏度的纳米结构表面进行表征，对于当前和下一代集成电路的发展至关重要。例如，现代的FinFET晶体管架构是通过光刻制造复杂的、有序的纳米结构来实现的。最近，针对此类应用提出了一种基于掠入射几何条件下X射线荧光测量的新型表征技术。该技术利用由入射辐射和反射辐射之间的干涉产生的X射线驻波场，作为纳米结构尺寸和成分参数的纳米级传感器。X射线荧光技术的元素灵敏度使得可以使用有限元方法重建空间元素分布。由于计算时间长，采用机器学习算法的智能优化方法对于及时提供结果至关重要。在这里，通过贝叶斯优化对概率分布进行采样不仅速度快，而且还提供了参数不确定性和灵敏度的初始估计。该方法的高灵敏度要求在对尺寸形状进行建模时精确了解材料参数，前提是材料的一些物理性质是已知的或事先已确定。未知的光学常数通过软X射线反射测量从未结构化但其他方面相同的层系统中提取。将光栅结构中包含的不同元素的空间分布轮廓与扫描电子显微镜和原子力显微镜进行了比较，并讨论了碳表面污染对建模结果的影响。这种新颖的方法能够以亚纳米分辨率对由氮化硅和氧化硅制成的纳米结构进行元素敏感且无损的表征。
