Application of Least-Squares Support Vector Machine (LS-SVM) to determination of deep level defect centers parameters in semi-insulating GaAs

摘要

The purpose of this paper is to present the Least Squares Support Vector Machine (LS-SVM) applied to investigation of deep level defects in semi-insulating gallium arsenide (SI GaAs). LS-SVM was used for spectral surface approximation, computed as a result of Photo Induced Transient Spectroscopy (HRPITS). Deep defects level parameters were extracted based on the spectral surface approximation and Arrhenius equation. Diverse LS-SVM modification was implemented to achieve good quality of estimation.