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 ...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 enhancedlight–matter interaction in nonlinearoptical 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.展开更多
基金supported by UNSW Scientia Fellowship and ARC Discovery Project(DP170103778)funding from ARC Discovery Early Career Research Fellowship(DE170100250)+1 种基金financial support from the Russian Foundation for Basic Research(Grants Nos.18-02-00381 and 19-02-00261)the Australian Research Council(DE19010043).
文摘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 enhancedlight–matter interaction in nonlinearoptical 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.
