Unwarping GISAXS data
Abstract
Grazing-incidence small-angle X-ray scattering (GISAXS) is a powerful technique for measuring the nanostructure of coatings and thin films. However, GISAXS data are plagued by distortions that complicate data analysis. The detector image is a warped representation of reciprocal space because of refraction, and overlapping scattering patterns appear because of reflection. A method is presented to unwarp GISAXS data, recovering an estimate of the true undistorted scattering pattern. The method consists of first generating a guess for the structure of the reciprocal-space scattering by solving for a mutually consistent prediction from the transmission and reflection sub-components. This initial guess is then iteratively refined by fitting experimental GISAXS images at multiple incident angles, using the distorted-wave Born approximation (DWBA) to convert between reciprocal space and detector space. This method converges to a high-quality reconstruction for the undistorted scattering, as validated by comparing with grazing-transmission scattering data. Furthermore, this new method for unwarping GISAXS images will broaden the applicability of grazing-incidence techniques, allowing experimenters to inspect undistorted visualizations of their data and allowing a broader range of analysis methods to be applied to GI data.
- Authors:
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1476690
- Alternate Identifier(s):
- OSTI ID: 1482567
- Report Number(s):
- BNL-209467-2018-JAAM
Journal ID: ISSN 2052-2525; IUCRAJ; PII: S2052252518012058
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Published Article
- Journal Name:
- IUCrJ
- Additional Journal Information:
- Journal Name: IUCrJ Journal Volume: 5 Journal Issue: 6; Journal ID: ISSN 2052-2525
- Publisher:
- International Union of Crystallography (IUCr)
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 77 NANOSCIENCE AND NANOTECHNOLOGY; GISAXS; X-ray scattering; GTSAXS; image healing; reconstruction; distorted-wave Born approximation
Citation Formats
Liu, Jiliang, and Yager, Kevin G. Unwarping GISAXS data. United Kingdom: N. p., 2018.
Web. doi:10.1107/S2052252518012058.
Liu, Jiliang, & Yager, Kevin G. Unwarping GISAXS data. United Kingdom. https://doi.org/10.1107/S2052252518012058
Liu, Jiliang, and Yager, Kevin G. Mon .
"Unwarping GISAXS data". United Kingdom. https://doi.org/10.1107/S2052252518012058.
@article{osti_1476690,
title = {Unwarping GISAXS data},
author = {Liu, Jiliang and Yager, Kevin G.},
abstractNote = {Grazing-incidence small-angle X-ray scattering (GISAXS) is a powerful technique for measuring the nanostructure of coatings and thin films. However, GISAXS data are plagued by distortions that complicate data analysis. The detector image is a warped representation of reciprocal space because of refraction, and overlapping scattering patterns appear because of reflection. A method is presented to unwarp GISAXS data, recovering an estimate of the true undistorted scattering pattern. The method consists of first generating a guess for the structure of the reciprocal-space scattering by solving for a mutually consistent prediction from the transmission and reflection sub-components. This initial guess is then iteratively refined by fitting experimental GISAXS images at multiple incident angles, using the distorted-wave Born approximation (DWBA) to convert between reciprocal space and detector space. This method converges to a high-quality reconstruction for the undistorted scattering, as validated by comparing with grazing-transmission scattering data. Furthermore, this new method for unwarping GISAXS images will broaden the applicability of grazing-incidence techniques, allowing experimenters to inspect undistorted visualizations of their data and allowing a broader range of analysis methods to be applied to GI data.},
doi = {10.1107/S2052252518012058},
journal = {IUCrJ},
number = 6,
volume = 5,
place = {United Kingdom},
year = {Mon Oct 08 00:00:00 EDT 2018},
month = {Mon Oct 08 00:00:00 EDT 2018}
}
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https://doi.org/10.1107/S2052252518012058
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