Issue 43, 2019

Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

Abstract

We demonstrate a tunable longwave infrared photodetector with ultra-high sensitivity based on graphene surface plasmon polaritons controlled by ferroelectric domains. The simulated results show that the photodetector shows a tunable absorption peak, modulated by periodically polarized ferroelectric domains at the nanoscale, with an ultra-high responsivity up to 7.62 × 106 A W−1 and a detectivity of ∼6.24 × 1013 Jones (Jones = cm Hz1/2 W−1) in the wavelengths ranging from 5 to 20 μm at room temperature. The potential mechanism for the prominent performances of the proposed photodetector can be attributed to the highly confined graphene surface plasmons excited by the local electrical field across the interface of the graphene and ferroelectric layer resonant to the incident wavelength, which could be easily controlled by the features of the ferroelectric domains. Compared with the silicon-based graphene plasmonic photodetector using a complex process of micro-nano fabrication, the proposed photodetector provides the advantages of a more convenient and controllable technique without the need for patterning graphene, and lower energy consumption due to the non-volatile properties of the ferroelectrics without an additional contact electrode. The tunable spectral response and the ultra-high responsivity make the graphene plasmonic photodetector tuned by the ferroelectric domains promising in practical applications of micro-spectrometers and other light sensing devices.

Graphical abstract: Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

Article information

Article type
Paper
Submitted
30 Jul 2019
Accepted
29 Sep 2019
First published
30 Sep 2019

Nanoscale, 2019,11, 20868-20875

Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

J. Guo, Y. Liu, Y. Lin, Y. Tian, J. Zhang, T. Gong, T. Cheng, W. Huang and X. Zhang, Nanoscale, 2019, 11, 20868 DOI: 10.1039/C9NR06508C

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