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Plasmonic Metasurfaces for Enhanced Pyroelectric Photodetection


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2021-09-02
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Date
2020
Author
Stewart, Jon William
Advisor
Mikkelsen, Maiken H
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Abstract

Thermal photodetectors are uniquely capable of sensing any incident wavelength and are only limited by the wavelength range of the integrated absorber, yet these detectors are commonly thought to be less sensitive and slower than their photoconductor (PC) or photovoltaic (PV) counterparts. As such, the use of thermal detectors for multispectral imaging has largely been ignored for spectral regions outside of the LWIR. However, recent theoretical and experimental developments have shown that thermal detectors with spectrally selective absorbers can outperform ideal PC and PV detectors in the MWIR and beyond. In this dissertation, thermal detectors integrated with spectrally selective, metasurface absorbers are theoretically and experimentally investigated. Large-area metasurface absorbers with resonances spanning from 340 to 2740 nm are developed that localize absorption and subsequent thermal generation in significantly subwavelength volumes. When integrated with a thermally sensitive pyroelectric film, the metasurface-pyroelectric detectors exhibit a sub-nanosecond response time with potential to match the response times of state-of-the-art, high-speed photodiodes. Overall, the spectrally selective thermal detectors developed here are a promising approach capable of disrupting the low cost, low form-factor spectral imaging market and providing an interesting platform to conduct research into photothermal generation and thermal diffusion at the nanoscale.

Description
Dissertation
Type
Dissertation
Department
Electrical and Computer Engineering
Subject
Electromagnetics
Optics
Thermodynamics
Multispectral
Nanophotonics
Photodetection
Photothermal Conversion
Plasmonic Metasurfaces
Pyroelectricity
Permalink
https://hdl.handle.net/10161/21445
Citation
Stewart, Jon William (2020). Plasmonic Metasurfaces for Enhanced Pyroelectric Photodetection. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/21445.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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