Journal of Terahertz Science and Electronic Information Technology , Volume. 23, Issue 6, 625(2025)

Multi-functional metasurface inverse design method based on Ultra-Wideband Spectrum prediction neural network

LI Yong1, ZHANG Yu2, YANG Guohui1、*, FU Jiahui1, ZHANG Kuang1, YUAN Yueyi1, and LI Yingsong3
Author Affiliations
  • 1School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150001, China
  • 2National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin Heilongjiang 150001, China
  • 3School of Electronic and Information Engineering, Anhui University, Hefei Anhui 230000, China
  • show less
    References(19)

    [1] [1] LUO Xiangang. Principles of electromagnetic waves in metasurfaces[J]. Science China Physics, Mechanics & Astronomy, 2015, 58(9): 594201. DOI: 10.1007/s11433-015-5688-1.

    [2] [2] YU N F, GENEVET P, KATS M A, et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction[J]. Science, 2011, 334(6054): 333-337. DOI: 10.1126/science.1210713.

    [3] [3] YUAN Y Y, CHEN S Q, RATNI B, et al. Bi-functional meta-device with full energy utilization in co-and cross-polarization fields[J]. Applied Physics Letters, 2020, 117(17): 171602. DOI: 10.1063/5.0022989.

    [4] [4] XIE Rensheng, XIN Minbo, CHEN Shiguo, et al. Frequency-multiplexed complex-amplitude meta-devices based on bispectral 2-bit coding meta-atoms[J]. Advanced Optical Materials, 2020, 8(24): 2000919. DOI: 10.1002/adom.202000919.

    [5] [5] YEUNG C, TSAI J M, KING B, et al. Multiplexed supercell metasurface design and optimization with tandem residual networks[J]. Nanophotonics, 2021, 10(3): 1133-1143. DOI: 10.1515/nanoph-2020-0549.

    [6] [6] AN S S, FOWLER C, ZHENG B W, et al. A deep learning approach for objective-driven all-dielectric metasurface design[J]. ACS Photonics, 2019, 6(12): 3196-3207. DOI: 10.1021/acsphotonics.9b00966.

    [7] [7] MALKIEL I, MREJEN M, NAGLER A, et al. Plasmonic nanostructure design and characterization via Deep Learning[J]. Light, Science & Applications, 2018(7): 60. DOI: 10.1038/s41377-018-0060-7.

    [8] [8] MMA Y H, KOLB J F, IHALAGE A A, et al. Incorporating meta-atom interactions in rapid optimization of large-scale disordered metasurfaces based on deep interactive learning[J]. Advanced Photonics Research, 2023, 4(4): 2200099. DOI: 10.1002/adpr.202200099.

    [9] [9] MA Qian, GAO Wei, XIAO Qiang, et al. Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform[J]. eLight, 2022, 2(1): 11. DOI: 10.1186/s43593-022-00019-x.

    [10] [10] AN S, ZHENG B, SHALAGINOV M Y, et al. Deep learning modeling approach for metasurfaces with high degrees of freedom[J]. Optics Express, 2020, 28(21): 31932. DOI: 10.1364/OE.401960.

    [11] [11] MA Ju, HUANG Yijia, PU Mingbo, et al. Inverse design of broadband metasurface absorber based on convolutional autoencoder network and inverse design network[J]. Journal of Physics D: Applied Physics, 2020, 53(46): 464002. DOI: 10.1088/1361-6463/aba3ec.

    [12] [12] LIU Z C, ZHU D Y, LEE K T, et al. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques[J]. Advanced Materials, 2020, 32(6): e1904790. DOI: 10.1002/adma.201904790.

    [13] [13] ADDABBO P, HAN S D, BIONDI F, et al. Adaptive radar detection in the presence of multiple alternative hypotheses using Kullback-Leibler information criterion―part II: applications[J]. IEEE Transactions on Signal Processing, 2021(69): 3742-3754. DOI: 10.1109/TSP.2021.3089277.

    [14] [14] GAHLMANN T, TASSIN P. Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials[J]. Physical Review B, 2022, 106(8): 085408. DOI: 10.1103/PhysRevB.106.085408.

    [15] [15] ZHANG Qian, LIU Che, WAN Xiang, et al. Machine-Learning designs of anisotropic digital coding metasurfaces[J]. Advanced Theory and Simulations, 2019, 2(2): 1800132. DOI: 10.1002/adts.201800132.

    [16] [16] ZHANG Jian, YUAN Jin, LI Chunzhen, et al. An inverse design framework for isotropic metasurfaces based on representation learning[J]. Electronics, 2022, 11(12): 1844. DOI: 10.3390/electronics11121844.

    [17] [17] KIANI M, KIANI J, ZOLFAGHARI M. Conditional generative adversarial networks for inverse design of multifunctional metasurfaces[J]. Advanced Photonics Research, 2022, 3(11): 2200110. DOI: 10.1002/adpr.202200110.

    [18] [18] ZHU Ruichao, QIU Tianshuo, WANG Jiafu, et al. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning[J]. Nature Communications, 2021, 12(1): 2974. DOI: 10.1038/s41467-021-23087-y.

    [19] [19] MANN S A, GOH H, AL A. Inverse design of nonlinear polaritonic metasurfaces for second harmonic generation[J]. ACS Photonics, 2023, 10(4): 993-1000. DOI: 10.1021/acsphotonics.2c01342.

    Tools

    Get Citation

    Copy Citation Text

    LI Yong, ZHANG Yu, YANG Guohui, FU Jiahui, ZHANG Kuang, YUAN Yueyi, LI Yingsong. Multi-functional metasurface inverse design method based on Ultra-Wideband Spectrum prediction neural network[J]. Journal of Terahertz Science and Electronic Information Technology , 2025, 23(6): 625

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Nov. 17, 2023

    Accepted: Jul. 30, 2025

    Published Online: Jul. 30, 2025

    The Author Email: YANG Guohui (h.yang@hit.edu.cn)

    DOI:10.11805/tkyda2023375

    Topics