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Proceedings Paper

Approximation of HRPITS results for SI GaAs by large scale support vector machine algorithms
Author(s): Stanisław Jankowski; Konrad Wojdan; Zbigniew Szymański; Roman Kozłowski
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Paper Abstract

For the first time large-scale support vector machine algorithms are used to extraction defect parameters in semi-insulating (SI) GaAs from high resolution photoinduced transient spectroscopy experiment. By smart decomposition of the data set the SVNTorch algorithm enabled to obtain good approximation of analyzed correlation surface by a parsimonious model (with small number of support vector). The extracted parameters of deep level defect centers from SVM approximation are of good quality as compared to the reference data.

Paper Details

Date Published: 12 October 2006
PDF: 8 pages
Proc. SPIE 6347, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2006, 634730 (12 October 2006); doi: 10.1117/12.714857
Show Author Affiliations
Stanisław Jankowski, Warsaw Univ. of Technology (Poland)
Konrad Wojdan, Warsaw Univ. of Technology (Poland)
Zbigniew Szymański, Warsaw Univ. of Technology (Poland)
Roman Kozłowski, Institute of Electronic Materials Technology (Poland)


Published in SPIE Proceedings Vol. 6347:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2006
Ryszard S. Romaniuk, Editor(s)

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