Optical metasurfaces, planar nanostructures comprised of subwavelength meta-atoms, have garnered significant attention for their remarkable ability to finely manipulate optical properties at the nanoscale. In this study, we present a dolmen-type gold metasurface featuring dual resonances designed to enhance second harmonic generation (SHG). Employing nonlinear scattering theory, our investigation explores the intricate interplay between Fano and surface lattice resonances (SLR), resulting in an extraordinary over 90-fold enhancement in SHG compared to single resonance scenarios. Our study paves the way for advancements on enhancing SHG of metallic metasurfaces, expanding their applications across various domains, including nonlinear optics, integrated optics, and neuromorphic photonics.
Optical metasurfaces, i.e., consisting of subwavelength meta-atoms, have emerged as a versatile platform for shaping light in both linear and nonlinear regimes. However, rare researches have focused on their saturable absorption properties and corresponding applications. Here we report a Q-switched polarization-maintaining fiber laser working at 1 μm based on double-rod gold metasurface as an SA. The periodic double-nanorod gold metasurface can provide a strong field enhancement by introducing Fano resonance and surface lattice resonance simultaneously, which is of benefit to significant nonlinear effects. Experimental results successfully confirm that the proposed gold metasurface can be implemented as a saturable absorber in a polarization-maintaining fiber laser for Q-switching operation. Typically, the passively Q-switched fiber laser could give output pulses at 1031.4 nm with a repetition rate of 34.1 kHz and a pulse width of 2.12 μs when pumped by a 976 nm laser diode of 155 mW. The repetition rate increases from 21.82 kHz to 35.76 kHz and the pulse width decreases from 3.38 μs to 1.92 μs when the pump power ranges from 105 mW to 160 mW.
We address inverse design of plasmonic Fano-resonant metasurfaces by using a tandem neural network (TNN) which can correctly predict both materials and structural parameters of target spectra. To train this TNN, 19530 groups of data from asymmetric double bar (ADB) nanostructures of varied dimensional parameters and different materials (Ag, Cu, and Al) respectively were collected. Our approach successfully addresses a non-uniqueness problem that commonly exists in nanophotonic inverse design. Besides, we choose target spectra generated outside the collected dataset in order to test the applicability and robustness of the TNN, which proves that the developed TNN is able to retrieve the nanoparticles of appropriate sizes and compositing material matching well Fano-profiled unknown target spectra within the spectral window of study.
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