Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-17T16:13:51.809Z Has data issue: false hasContentIssue false
  • English
  • Français

Determination of Z-Profiles of Diffraction Data from τ-Profiles Using a Numerical Linear Inversion Method

Published online by Cambridge University Press:  06 March 2019

Xiaojing Zhu ,
Benjamin Ballard  and
Paul Predecki
Xiaojing Zhu
Affiliation:
Department of Engineering, University of Denver Denver, CO 80208, USA
Benjamin Ballard
Affiliation:
Department of Engineering, University of Denver Denver, CO 80208, USA
Paul Predecki
Affiliation:
Department of Engineering, University of Denver Denver, CO 80208, USA
Get access

Abstract

A numerical procedure known as the constrained linear inversion method is employed for obtaining the z-profiles of diffraction data from their τ-profiles. The method was first tested with known z-profiles and gave good agreement with the starting z-profiles even with up to 5% random noise introduced into the τ-profile or up to 10% error in specimen thickness. The procedure was then applied to an experimentally obtained strain profile and a non-oscillatory z-profile was calculated. Although the z-profile obtained could not be verified by an independent method, the success of tests with known z-profiles leads to increased confidence in the numerical method described.

Type
III. Applications of Diffraction to Semiconductors and Films
Information
Advances in X-Ray Analysis , Volume 38: Forty-third Annual Conference on Applications of X-ray Analysis , 1994 , pp. 255 - 262
Copyright
Copyright © International Centre for Diffraction Data 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Doerner, M.F. and Brennan, S., J. Appl. Physics., 63, 126–131, (1988).Google Scholar
2. Shute, C.J. and Cohen, J.B., J. Appl. Phys., 70(4), 2104–2110, (1991).Google Scholar
3. Doile, H. and Hauk, V., Hartcrei Techn Mitt., 31, 165 (1976).Google Scholar
4. Wolfstieg, U., Harterei-Techn. Mitt. 31, 83 (1976).Google Scholar
5. Parratt, L.G., Phys.Rev., 95, 359–369 (1954).Google Scholar
6. Zhu, X. and Predecki, P” Advances in X-ray Analysis, 37, 197–203, (1994).Google Scholar
7. Press, W.H. et al., Numerical Recipes, The Art of Scientific Computing, 2nd Ed., Cambridge University Press, (1992).Google Scholar
8. Twomey, S., J. Association for Computing Machinery, 10, 97–101, (1963).Google Scholar
9. Twomey, S., Inversion Method in Atmospheric Remote Sounding, 41, (1977).Google Scholar
10. Zhu, X., Predecki, P. and Ballard, B., Paper elsewhere in these proceedings.Google Scholar
11. Ballard, B. and Predecki, P., Advances in X-ray Analysis, 37, 189–196, (1994).Google Scholar
12. Fleming, H., Inversion Method in Atmospheric Remote Sounding, 325, (1977).Google Scholar