Abstract
This paper presents the advantages of the application of a computational intelligence algorithm to extracting trap parameters for semi-insulating GaAs single crystals. The defect centres are investigated by the photoinduced transient spectroscopy. The defect levels manifest themselves as the folds on the Laplace surfaces. The application of the Relevance Vector Machine allows obtaining optimal sparse approximation (maximum accuracy at the minimum number of components) of the experimental surface. In order to determine the parameters of defect centres (activation energy and the pre-exponential factor in the Arrhenius formula), the processing of the experimental surface consisting of 300,000 data points takes approximately 1 h. By means of the new algorithm, 14 defect centres with activation energies ranging from 185 to 805 meV were resolved in a SI GaAs single crystal grown by the vertical gradient freeze (VGF) method.
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