journal article·2021-11

Observation of negative photoconductivity at bandgap and super bandgap excitations in GaN nanorods

Shuchi Kaushik, Ashok Kumar Kapoor, Rohit Kumar Pant, Saluru Baba Krupanidhi, Rajendra Singh

Optical Materials· Volume 121
Publisher page

Abstract

Abstract We report on the surprising observation of negative photoconductivity (NPC) in GaN nanorods (NRs) by bandgap and super bandgap excitations. A metal-semiconductor-metal (MSM) type of device configuration using Ni/Au as an electrode at both ends, has been used to study the NPC phenomenon. On exposing the device to the light sources with 363 nm (bandgap excitation) and 250 nm (super bandgap excitation) wavelengths, NPC with a drop of approximately 50 and 60% was observed, respectively. To explain this phenomenon, a model of trapping of photogenerated electrons by the trap states is proposed. Interestingly, a significant drop in the illumination current is observed at low values of power density (up to 3 μW/mm2) of the incident light. It has been interpreted that on increasing the power density, the density of trapped electrons starts approaching the density of available trap states. The de-trapping process is observed to be very slow as the illumination current reached the dark current value in nearly 2 h after switching off the incident light. The observation of NPC and saturation of trap states with high intensity of incident light are also validated by Kelvin Probe Force Microscopy (KPFM) characterization performed in the dark and on illumination with varying intensity of the incident light. Furthermore, on increasing the temperature from room temperature (RT) to 300 °C, the NPC was found to reduce to almost zero, which indicates the de-trapping of trapped electrons on increasing the temperature.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: GaN-based semiconductor devices and materials · Ga2O3 and related materials · Nanowire Synthesis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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