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Title: 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. https://doi.org/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 = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1107/S2052252518012058

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Cited by: 1 work
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