291 research outputs found
Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach
A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters, such as various materials’ properties and coupling effects, as well as the geometrical parameters. Ideally, this would require multidimensional space optimization through direct numerical simulations. Recently, an alternative, popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method. We utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position. We exploit such high-Q resonances for enhanced light–matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation; at the same time, they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Metasurfaces have widespread applications in fifth-generation (5G) microwave
communication. Among the metasurface family, free-form metasurfaces excel in
achieving intricate spectral responses compared to regular-shape counterparts.
However, conventional numerical methods for free-form metasurfaces are
time-consuming and demand specialized expertise. Alternatively, recent studies
demonstrate that deep learning has great potential to accelerate and refine
metasurface designs. Here, we present XGAN, an extended generative adversarial
network (GAN) with a surrogate for high-quality free-form metasurface designs.
The proposed surrogate provides a physical constraint to XGAN so that XGAN can
accurately generate metasurfaces monolithically from input spectral responses.
In comparative experiments involving 20000 free-form metasurface designs, XGAN
achieves 0.9734 average accuracy and is 500 times faster than the conventional
methodology. This method facilitates the metasurface library building for
specific spectral responses and can be extended to various inverse design
problems, including optical metamaterials, nanophotonic devices, and drug
discovery
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