1 / 12

NCNR Summer School '06

NCNR Summer School '06. Reflectometry Reduction and Analysis. Paul Kienzle paul.kienzle@nist.gov. Experimental Setup. Polarizer and Flipper (+/ − ). Detector. Polarizer and Flipper (+/ − ). Slit 4. θ 2. Slit 3. Sample. Detector. I. Specular Scan θ 2 = 2 θ. Log I. Slit Scan

Télécharger la présentation

NCNR Summer School '06

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. NCNR Summer School '06 Reflectometry Reduction and Analysis Paul Kienzle paul.kienzle@nist.gov

  2. Experimental Setup

  3. Polarizer and Flipper (+/−) Detector Polarizer and Flipper (+/−) Slit 4 θ2 Slit 3 Sample Detector I Specular Scan θ2 = 2θ Log I Slit Scan θ2 = 0 Rocking Curve θ or θ2 fixed I θ Fixed slits Q Z Log I Background Scan θ2 ≠ 2θ Q Z Repeat each curve for: +− −− B= A= −+ D= ++ C= Q Z Data Reduction White Beam Monochromator Slit 1 Slit 2 θ

  4. What is it good for? • Subsurface structure up to 1μm • Polymers, biofilms, magnetic surfaces, ... • Determines average density at depth z

  5. z where translates reflectivity into lab frame Oscillations in reflectivity R(Q) of period Optical Matrix Formalism

  6. Fitted Data

  7. χ2 Landscape (ρ2 vs d2)

  8. χ2 Landscape (d2 vs d3)

  9. 170 0.0085 ≈2π/740 710 0.035 ≈2π/180 Heuristics

  10. Prior Knowledge

  11. Simultaneous Fitting

  12. Our Problem • Many local minima • 'Garden Path' fit space • Expensive objective function • Continuous but no analytic derivative • Significant number of parameters • ... but many priors • E.g., known material, known sputtering time, information from other measurements, theoretical models, bounds constraints • There is hope for ye who enter.

More Related