Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

Lei Xu; Mohsen Rahmani; Yixuan Ma; Daria A. Smirnova; Khosro Zangeneh Kamali; Fu Deng; Yan Kei Chiang; Lujun Huang; Haoyang Zhang; Stephen Gould; Dragomir N. Neshev; Andrey E. Miroshnichenko

doi:10.1117/1.AP.2.2.026003

Journals >Advanced Photonics >Volume 2 >Issue 2 >Page 026003 > Article

Advanced Photonics
Vol. 2, Issue 2, 026003 (2020)

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach | Article Video

Lei Xu^1、2, Mohsen Rahmani^{2、3、4、*}, Yixuan Ma¹, Daria A. Smirnova³, Khosro Zangeneh Kamali^3、4, Fu Deng¹, Yan Kei Chiang¹, Lujun Huang¹, Haoyang Zhang⁵, Stephen Gould⁶, Dragomir N. Neshev^3、4, and Andrey E. Miroshnichenko^1、*

Author Affiliations

¹University of New South Wales, School of Engineering and Information Technology, Canberra, Australia

²Nottingham Trent University, School of Science & Technology, Department of Engineering, Advanced Optics and Photonics Laboratory, Nottingham, United Kingdom

³Australian National University, Research School of Physics, Nonlinear Physics Centre, Canberra, Australia

⁴Australian National University, Research School of Physics, ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS), Canberra, Australia

⁵Queensland University of Technology, School of Electrical Engineering and Computer Science, Brisbane, Queensland, Australia

⁶Australian National University, College of Engineering and Computer Science, Canberra, Australia

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DOI: 10.1117/1.AP.2.2.026003 Cite this Article Set citation alerts

Lei Xu, Mohsen Rahmani, Yixuan Ma, Daria A. Smirnova, Khosro Zangeneh Kamali, Fu Deng, Yan Kei Chiang, Lujun Huang, Haoyang Zhang, Stephen Gould, Dragomir N. Neshev, Andrey E. Miroshnichenko. Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach[J]. Advanced Photonics, 2020, 2(2): 026003 Copy Citation Text

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(a) Top: (top left) Schematics of the silicon nanobars metasurface and (top right) its unit cell. Bottom: Calculated transmission spectrum of the metasurface with structural parameters w=316 nm, L=580 nm, x0=189 nm. (b) Spherical multipolar structure of the metasurface. (c) Top: Cartesian ED and TD modes excitations. Bottom: The electric energy enhancement ηE/ηE0. It is defined as the electric energy inside the two nanobars normalized by the electric energy within the same volume of the nanobars for the pump field. (d) Electric near-field distributions at the resonance. Left: 3-D view. Right: top view.

Fig. 1. (a) Top: (top left) Schematics of the silicon nanobars metasurface and (top right) its unit cell. Bottom: Calculated transmission spectrum of the metasurface with structural parameters

w = 316 nm

,

L = 580 nm

,

x_{0} = 189 nm

. (b) Spherical multipolar structure of the metasurface. (c) Top: Cartesian ED and TD modes excitations. Bottom: The electric energy enhancement

η_{E} / η_{E_{0}}

. It is defined as the electric energy inside the two nanobars normalized by the electric energy within the same volume of the nanobars for the pump field. (d) Electric near-field distributions at the resonance. Left: 3-D view. Right: top view.

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The architecture of the TN model, which consists of an inverse-design network connected to a pretrained forward model network. X represents the input and output, which is the transmission spectra data in our case, and Y represents the output in the middle layer which is the structural parameters here.

Fig. 2. The architecture of the TN model, which consists of an inverse-design network connected to a pretrained forward model network.

X

represents the input and output, which is the transmission spectra data in our case, and

Y

represents the output in the middle layer which is the structural parameters here.

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Fig. 3. (a) Evolution of the training loss for the forward model network. (b) Comparison of the NN approximation to the real transmission spectrum. (c) Evolution of the training loss for the inverse-design model network. (d) Comparison of the spectra between the NN approximation and the input based on Eq. (2).

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Fig. 4. Inverse design of Si nanobar metasurfaces with Fano-shape transmission spectra. (a)–(c)

λ_{0} = 1450

, 1500, and 1550 nm, respectively.

Δ λ = 15 nm

,

q = 0.8

. (d)–(f)

λ_{0} = 1500 nm

,

Δ λ = 10 nm

,

q = 0.3

, 0.5, and 0.7, respectively. (g)–(i)

λ_{0} = 1500 nm

,

Δ λ = 5

, 15, and 25 nm, respectively,

q = 0.7

.

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Fig. 5. (a) SEM image of the fabricated sample with designed resonance at 1500 nm. (b) Experimentally measured linear spectra. (c) Experimentally measured THG spectra of the samples.

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Fig. 6. (a)–(c) Optomechanic vibration under the

y

-polarized pump. (a) Displacement of the nanobars after 1 ns. (b) The transient displacement

D_{x}

and

D_{y}

. (c) Spectral densities of displacement

D_{x}

and

D_{y}

. (d)–(f) Optomechanical vibration under the

x

-polarized pump. (d) Displacement of the nanobars after 1 ns. (e) The transient displacement

D_{x}

and

D_{y}

. (f) Spectral densities of displacement

D_{x}

and

D_{y}

.

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Fig. 7. The spectral density of

D

in the (a)

x

and (b)

y

directions for different laser pump wavelengths.

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Lei Xu, Mohsen Rahmani, Yixuan Ma, Daria A. Smirnova, Khosro Zangeneh Kamali, Fu Deng, Yan Kei Chiang, Lujun Huang, Haoyang Zhang, Stephen Gould, Dragomir N. Neshev, Andrey E. Miroshnichenko. Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach[J]. Advanced Photonics, 2020, 2(2): 026003

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