Efficient defect structure analysis in semi-insulating materials by support vector machine and relevance vector machine

Details Efficient defect structure analysis in semi-insulating materials by support vector machine and relevance vector machine Efficient defect structure analysis in semi-insulating materials by support vector machine and relevance vector machine

Jan 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6937e..66j&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007. Edited by Romaniuk, Rys

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We propose new approach for defect centers parameters extraction in semi-insulating GaAs. The experimental data is obtained by high-resolution photoinduced transient spectroscopy (HR-PITS). Two algorithms have been introduced: support vector machine - sequential minimal optimization (SVM-SMO) and relevance vector machine (RVM). Those methods perform the approximation of the Laplace surface. The advantages of proposed methods are: good accuracy of approximation, low complexity, excellent generalization. We developed SVM-RVM-PITS system, which enables graphical representation of Laplace surface, defining local area for defect parameter extraction, choosing the SVM or RVM method for approximation, calculation of the Arrhenius line factors and finally the parameters of the defect centers.

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