TY - JOUR
T1 - Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach
AU - Xu, Lei
AU - Rahmani, Mohsen
AU - Ma, Yixuan
AU - Smirnova, Daria A.
AU - Kamali, Khosro Zangeneh
AU - Deng, Fu
AU - Chiang, Yan Kei
AU - Huang, Lujun
AU - Zhang, Haoyang
AU - Gould, Stephen
AU - Neshev, Dragomir N.
AU - Miroshnichenko, Andrey E.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Fano resonance
KW - dielectric nanostructures
KW - machine learning
KW - optoacoustics
KW - third-harmonic generation
UR - http://www.scopus.com/inward/record.url?scp=85118842899&partnerID=8YFLogxK
U2 - 10.1117/1.AP.2.2.026003
DO - 10.1117/1.AP.2.2.026003
M3 - Comment/debate
SN - 2577-5421
VL - 2
JO - Advanced Photonics
JF - Advanced Photonics
IS - 2
M1 - 026003
ER -