Characterization of surfaces using neural pattern recognition methods based on BRDF and AFM measurements

Thomas Rinder; Hendrik Rothe

doi:10.1117/12.366693

25 October 1999 Characterization of surfaces using neural pattern recognition methods based on BRDF and AFM measurements

Thomas Rinder, Hendrik Rothe

Author Affiliations +

Proceedings Volume 3784, Rough Surface Scattering and Contamination; (1999) https://doi.org/10.1117/12.366693
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

A major problem of in-situ surface characterization by using angle-resolved light scattering is the fast and accurate surface parameter identification. This paper will deal with surface parameter identification methods from BRDF measurements of rough surfaces with stochastical height topographies. First, neural classification methods will be discussed. Second, the discussed classification method will be applied to BRDF data taken by an ARS silicon sensor with 8013 polar photodiodes. The classification results will be compared to topography data taken from AFM measurements. Finally, neural self-organized networks will be applied to classify in unsupervised manner rough surfaces based on BRDF measurements.

Citation Download Citation

Thomas Rinder and Hendrik Rothe "Characterization of surfaces using neural pattern recognition methods based on BRDF and AFM measurements", Proc. SPIE 3784, Rough Surface Scattering and Contamination, (25 October 1999); https://doi.org/10.1117/12.366693

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