Nonlinear and Chaotic Charge Transport in Semi-Insulating Semiconductors

Abstract

Recent experimental results on spontaneous periodic and chaotic low frequency current oscillations in semi-insulating crystalline semiconductors are reviewed. Period-doubling bifurcations and intermittent chaotic behavior were observed, depending on the value of the applied voltage. The dependencies of the fundamental frequency of low frequency current oscillations on the applied voltage were found to be sharply nonmonotonical in the samples of GaAs with controlled and different EL2 defect concentrations. Temperature dependencies of frequency modes (f) on an Arrhenius plot of log(T2/2f) vs. 1/T were found to give information on deep levels in InP.The experimental results of nonlinear charge transport and low frequency current oscillations in spatially chaotic polycrystalline and nanostructured fractal-like porous Si are also presented. The dependencies of the fundamental frequency on the applied voltage were found to be monotonically increasing in these materials. The transition to chaotic behavior through quasi-periodic route was observed in polycrystalline Si. Semi-insulating polycrystalline Si and porous Si are likely to present a new class of materials for experimental investigations of phenomena concerning nonlinear dynamics.A model of low frequency current oscillations in semi-insulating crystalline semiconductors with deep levels has been developed. A combination of the approaches of field-enhanced trapping, field-enhanced emission and negative differential mobility of carriers was used. A linear analysis of the model was done. The dispersion relations of the model were found to describe qualitatively the experimentally measured dependencies of the activation energies and of the fundamental frequency on the electric field and the temperature.KeywordsApplied VoltageFundamental FrequencyCapture Cross SectionHigh Electric FieldChaotic OscillationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.