Abstract
Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.
Highlights
Enhanced generation of hot-electrons is of particular interest as these electrons can be harnessed for various applications including photodetection [1], photochemistry [2,3], solar cells [4], imaging [5], sensing [6,7], hydrogen generation [8,9], and CO2 reduction [10]
We used an artificial intelligence-based approach to optimize and predict the absorption spectra of plasmonic metasurfaces fabricated on top of a semiconductor
Our metasurface design does not require overlapping of two resonances and yet it achieved a similar absorption as that in [19], which resulted in hot-electron photodetectors with record high photosensitivities
Summary
Enhanced generation of hot-electrons is of particular interest as these electrons can be harnessed for various applications including photodetection [1], photochemistry [2,3], solar cells [4], imaging [5], sensing [6,7], hydrogen generation [8,9], and CO2 reduction [10]. Photocurrent is generated from the injection of the hot-electrons, with the required momentum distribution, into the conduction band of the semiconductor through an internal photoemission process Because this operational principle is fundamentally different, the operational wavelength of hot-electron photodetectors is instead determined by the height of the Schottky barrier and not the intrinsic bandgap of the semiconductor. [18,19], large improvements in the efficiency of photodetection have been reported by designing hot-electron based photodetectors with optimized absorption. We used an artificial intelligence-based approach to optimize and predict the absorption spectra of plasmonic metasurfaces fabricated on top of a semiconductor. Our metasurface design does not require overlapping of two resonances and yet it achieved a similar absorption as that in [19], which resulted in hot-electron photodetectors with record high photosensitivities. Just a proof-of-concept, the results of the absorption spectra predicted by the neural network were well-matched with the electromagnetic simulations, thereby suggesting a new direction for artificial intelligence-assisted optimization of optical absorption in plasmonic metasurfaces
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