Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network

Yi Xu; Fu Li; Jianqiang Gu; Zhiwei Bi; Bing Cao; Quanlong Yang; Jiaguang Han; Qinghua Hu; Weili Zhang

doi:10.1117/1.APN.3.2.026002

Advanced Photonics Nexus, Volume. 3, Issue 2, 026002(2024)

Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network

Yi Xu¹, Fu Li¹, Jianqiang Gu^1、*, Zhiwei Bi², Bing Cao^2、*, Quanlong Yang^3、*, Jiaguang Han⁴, Qinghua Hu², and Weili Zhang⁵

Author Affiliations

¹Tianjin University, Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering, Ministry of Education, Key Laboratory of Optoelectronic Information Technology, Tianjin, China

²Tianjin University, College of Intelligence and Computing, Tianjin, China

³Central South University, School of Physics and Electronics, Hunan Key Laboratory of Nanophotonics and Devices, Changsha, China

⁴Guilin University of Electronic Technology, School of Optoelectronic Engineering, Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin, China

⁵Oklahoma State University, School of Electrical and Computer Engineering, Stillwater, Oklahoma, United States

show less

References(42)

[1] H. T. Chen, A. J. Taylor, N. F. Yu. A review of metasurfaces: physics and applications. Rep. Prog. Phys., 79, 076401(2016).

[2] F. Aieta et al. Aberration-free ultrathin flat lenses and axicons at telecom wavelengths based on plasmonic metasurfaces. Nano Lett., 12, 4932-4936(2012).

[3] M. Khorasaninejad et al. Polarization-insensitive metalenses at visible wavelengths. Nano Lett., 16, 7229-7234(2016).

[4] Y. J. Wang et al. High-efficiency broadband achromatic metalens for near-IR biological imaging window. Nat. Commun., 12, 5560(2021).

[5] Q. L. Yang et al. Broadband and robust metalens with nonlinear phase profiles for efficient terahertz wave control. Adv. Opt. Mater., 5, 1601084(2017).

[6] S. L. Sun et al. High-efficiency broadband anomalous reflection by gradient meta-surfaces. Nano Lett., 12, 6223-6229(2012).

[7] Q. Yang et al. Broadband terahertz rotator with an all-dielectric metasurface. Photonics Res., 6, 1056-1061(2018).

[8] K. E. Chong et al. Polarization-independent silicon metadevices for efficient optical wavefront control. Nano Lett., 15, 5369-5374(2015).

[9] D. Hakobyan et al. Tailoring orbital angular momentum of light in the visible domain with metallic metasurfaces. Adv. Opt. Mater., 4, 306-312(2016).

[10] H. F. Zhang et al. Polarization-independent all-silicon dielectric metasurfaces in the terahertz regime. Photonics Res., 6, 24-29(2018).

[11] A. Arbabi et al. Dielectric metasurfaces for complete control of phase and polarization with subwavelength spatial resolution and high transmission. Nat. Nanotechnol., 10, 937-943(2015).

[12] P. C. Wu et al. Versatile polarization generation with an aluminum plasmonic metasurface. Nano Lett., 17, 445-452(2017).

[13] J. P. B. Mueller et al. Metasurface polarization optics: Independent phase control of arbitrary orthogonal states of polarization. Phys. Rev. Lett., 118, 113901(2017).

[14] W. T. Chen et al. High-efficiency broadband meta-hologram with polarization-controlled dual images. Nano Lett., 14, 225-230(2014).

[15] Z. Wang et al. Huygens metasurface holograms with the modulation of focal energy distribution. Adv. Opt. Mater., 6, 1800121(2018).

[16] Q. Wang et al. Polarization and frequency multiplexed terahertz meta-holography. Adv. Opt. Mater., 5, 1700277(2017).

[17] O. Khatib et al. Deep learning the electromagnetic properties of metamaterials—a comprehensive review. Adv. Func. Mater., 31(2021).

[18] J. Q. Jiang, M. K. Chen, J. A. Fan. Deep neural networks for the evaluation and design of photonic devices. Nat. Rev. Mater., 6, 679-700(2021).

[19] M. K. Chen et al. Artificial intelligence in meta-optics. Chem. Rev., 122, 15356-15413(2022).

[20] W. Ma et al. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2020).

[21] Y. Xu et al. Software-defined nanophotonic devices and systems empowered by machine learning. Prog. Quantum Electron., 89, 100469(2023).

[22] Y. Xu et al. Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks. Photonics Res., 9, B135-B152(2021).

[23] W. Ma et al. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv. Mater., 31, 1901111(2019).

[24] W. Ma et al. Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater., 34, 2110022(2022).

[25] S. An et al. A deep learning approach for objective-driven all-dielectric metasurface design. ACS Photonics, 6, 3196-3207(2019).

[26] O. Khatib et al. Learning the physics of all-dielectric metamaterials with deep lorentz neural networks. Adv. Opt. Mater., 10, 2200097(2022).

[27] Z. Liu et al. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018).

[28] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326-6334(2018).

[29] I. Malkiel et al. Plasmonic nanostructure design and characterization via deep learning. Light-Sci. Appl., 7, 60(2018).

[30] Z. A. Kudyshev et al. Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization. Appl. Phys. Rev., 7, 021407(2020).

[31] H. Ren et al. Three-dimensional vectorial holography based on machine learning inverse design. Sci. Adv., 6, eaaz4261(2020).

[32] Y. Qu et al. Migrating knowledge between physical scenarios based on artificial neural networks. ACS Photonics, 6, 1168-1174(2019).

[33] D. Xu et al. Efficient design of a dielectric metasurface with transfer learning and genetic algorithm. Opt. Mater. Exp., 11(2021).

[34] J. Zhang et al. Heterogeneous transfer-learning-enabled diverse metasurface design. Adv. Opt. Mater., 10, 2200748(2022).

[35] R. Zhu et al. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning. Nat. Commun., 12, 2974(2021).

[36] Z. Zhao et al. Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials. Nanophotonics, 11, 4465-4478(2022).

[37] I. Tanriover, W. Hadibrata, K. Aydin. Physics-based approach for a neural networks enabled design of all-dielectric metasurfaces. ACS Photonics, 7, 1957-1964(2020).

[38] C. Trabelsi et al. Deep complex networks(2017).

[39] J. Yosinski et al. How transferable are features in deep neural networks?(2014).

[40] D. Liu et al. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics, 5, 1365-1369(2018).

[41] L. Xu et al. Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach. Adv. Photonics, 2, 026003(2020).

[42] P. R. Wiecha et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res., 9, B182-B200(2021).

Tools

Get Citation

Copy Citation Text

Yi Xu, Fu Li, Jianqiang Gu, Zhiwei Bi, Bing Cao, Quanlong Yang, Jiaguang Han, Qinghua Hu, Weili Zhang, "Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network," Adv. Photon. Nexus 3, 026002 (2024)

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Research Articles

Received: Oct. 9, 2023

Accepted: Jan. 16, 2024

Published Online: Apr. 2, 2024

The Author Email: Gu Jianqiang (gjq@tju.edu.cn), Cao Bing (caobing@tju.edu.cn), Yang Quanlong (quanlong.yang@csu.edu.cn)

DOI:10.1117/1.APN.3.2.026002

CSTR:32397.14.1.APN.3.2.026002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology