Characterization Of Nanostructured Surfaces Research Articles

Shape- and Element-Sensitive Reconstruction of Periodic Nanostructures with Grazing Incidence X-ray Fluorescence Analysis and Machine Learning.

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.

Characterization of nano-structured surfaces by EUV scatterometry

Scatterometry in the UV and the VIS is widely used for the inspection of nano-structured surfaces in, e.g. quantitative wafer metrology in the semiconductor industry. As the structures become progressively smaller, ever fewer propagating diffraction orders exist. Consequently, these few orders do not carry enough information about the structure any more. Particularly the reconstruction of structures without detailed a priori knowledge becomes impossible due to this lack of information. The Physikalisch-Technische Bundesanstalt (PTB) has developed extreme UV (EUV) scatterometry using its EUV reflectometry facility at the electron storage ring BESSY II. The short wavelength of EUV at around 13 nm is advantageous since it provides more propagating diffraction orders compared to the longer wavelength radiation. The short wavelength also increases the sensitivity to small structural features, particularly roughness. We present the application of EUV scatterometry for the characterization of absorber lines with a trapezoidal cross section on semiconductor photo masks and for the characterization of ultra-smooth surfaces of EUV multilayer mirrors. It is shown that structure roughness significantly impacts the scattered diffraction intensities from structured surfaces and must be included in the structure reconstruction algorithms using inverse modelling by FEM. Estimates for the changes in reconstructed geometrical parameters induced by roughness are presented.