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A multi-slice simulation algorithm for grazing-incidence small-angle X-ray scattering

  • Author(s): Venkatakrishnan, SV
  • Donatelli, J
  • Kumar, D
  • Sarje, A
  • Sinha, SK
  • Li, XS
  • Hexemer, A
  • et al.
Abstract

© International Union of Crystallography, 2016. Grazing-incidence small-angle X-ray scattering (GISAXS) is an important technique in the characterization of samples at the nanometre scale. A key aspect of GISAXS data analysis is the accurate simulation of samples to match the measurement. The distorted-wave Born approximation (DWBA) is a widely used model for the simulation of GISAXS patterns. For certain classes of sample such as nanostructures embedded in thin films, where the electric field intensity variation is significant relative to the size of the structures, a multi-slice DWBA theory is more accurate than the conventional DWBA method. However, simulating complex structures in the multi-slice setting is challenging and the algorithms typically used are designed on a case-by-case basis depending on the structure to be simulated. In this paper, an accurate algorithm for GISAXS simulations based on the multi-slice DWBA theory is presented. In particular, fundamental properties of the Fourier transform have been utilized to develop an algorithm that accurately computes the average refractive index profile as a function of depth and the Fourier transform of the portion of the sample within a given slice, which are key quantities required for the multi-slice DWBA simulation. The results from this method are compared with the traditionally used approximations, demonstrating that the proposed algorithm can produce more accurate results. Furthermore, this algorithm is general with respect to the sample structure, and does not require any sample-specific approximations to perform the simulations. This paper presents an accurate numerical algorithm for simulating grazing-incidence small-angle X-ray scattering patterns of nanostructures using the multi-slice distorted-wave Born approximation. The method overcomes the typical challenge of requiring the users to manually specify a way to approximate their samples by utilizing properties of Fourier transforms to automate the computation.

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