P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6(8), 679–700 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

R. Li, X. Gu, K. Li, Y. Huang, Z. Li, and Z. Zhang, “Deep learning-based modeling of photonic crystal nanocavities,” Opt. Mater. Express 11(7), 2122–2133 (2021).
[Crossref]

Y. Lin, Y. Han, C. Song, and Y. Deng, “Topologically optimized periodic resonant nanostructures for extraordinary optical transmission,” Opt. Mater. Express 11(7), 2153–2164 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

S. Noureen, M. Zubair, M. Ali, and M. Q. Mehmood, “Deep learning based hybrid sequence modeling for optical response retrieval in metasurfaces for STPV applications,” Opt. Mater. Express 11(9), 3178–3193 (2021).
[Crossref]

A. Burgess and M. Florescu, “Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks,” Opt. Mater. Express 11(7), 2037–2048 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6(8), 679–700 (2021).
[Crossref]

J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6(8), 679–700 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6(8), 679–700 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Y. Lin, Y. Han, C. Song, and Y. Deng, “Topologically optimized periodic resonant nanostructures for extraordinary optical transmission,” Opt. Mater. Express 11(7), 2153–2164 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

Z. Liu, D. Zhu, L. Raju, and W. Cai, “Tackling photonic inverse design with machine learning,” Adv. Sci. 8(5), 2002923 (2021).
[Crossref]

S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9(5), 1041–1057 (2020).
[Crossref]

W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).
[Crossref]

J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6(8), 679–700 (2021).
[Crossref]

Q. Wu, X. Li, L. Jiang, X. Xu, D. Fang, J. Zhang, C. Song, Z. Yu, L. Wang, and L. Gao, “Deep neural network for designing near- and far-field properties in plasmonic antennas,” Opt. Mater. Express 11(7), 1907–1917 (2021).
[Crossref]

R. Li, X. Gu, K. Li, Y. Huang, Z. Li, and Z. Zhang, “Deep learning-based modeling of photonic crystal nanocavities,” Opt. Mater. Express 11(7), 2122–2133 (2021).
[Crossref]

Y. Lin, Y. Han, C. Song, and Y. Deng, “Topologically optimized periodic resonant nanostructures for extraordinary optical transmission,” Opt. Mater. Express 11(7), 2153–2164 (2021).
[Crossref]

J. Noh, Y.-H. Nam, S. So, C. Lee, S.-G. Lee, Y. Kim, T.-H. Kim, J.-H. Lee, and J. Rho, “Design of a transmissive metasurface antenna using deep neural networks,” Opt. Mater. Express 11(7), 2310–2317 (2021).
[Crossref]

S. Noureen, M. Zubair, M. Ali, and M. Q. Mehmood, “Deep learning based hybrid sequence modeling for optical response retrieval in metasurfaces for STPV applications,” Opt. Mater. Express 11(9), 3178–3193 (2021).
[Crossref]

A. Burgess and M. Florescu, “Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks,” Opt. Mater. Express 11(7), 2037–2048 (2021).
[Crossref]

P. R. Wiecha, A. Arbouet, C. Girard, and O. L. Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Res. 9(5), B182–B200 (2021).
[Crossref]

Y. Xu, X. Zhang, Y. Fu, and Y. Liu, “Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks,” Photonics Res. 9(4), B135–B152 (2021).
[Crossref]