[1] N. Yu, P. Genevet, M. A. Kats. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science, 334, 333-337(2011).

[2] L. Huang, X. Chen, H. Muhlenbernd. Dispersionless phase discontinuities for controlling light propagation. Nano Lett., 12, 5750-5755(2012).

[3] V. Neder, Y. Ra’di, A. Alù. Combined metagratings for efficient broad-angle scattering metasurface. ACS Photonics, 6, 1010-1017(2019).

[4] Z. Zhang, M. Kang, X. Zhang. Coherent perfect diffraction in metagratings. Adv. Mater., 32, 2002341(2020).

[5] C. McDonnell, J. Deng, S. Sideris. Terahertz metagrating emitters with beam steering and full linear polarization control. Nano Lett., 22, 2603-2610(2022).

[6] R. Aoni, S. Manjunath, B. Karawdeniya. Resonant dielectric metagratings for response intensified optical sensing. Adv. Funct. Mater., 32, 2103143(2022).

[7] Z. Liu, C. Zhang, W. Zhu. Compact stereo waveguide display based on a unidirectional polarization-multiplexed metagrating in-coupler. ACS Photonics, 8, 1112-1119(2021).

[8] H. Liu, F. Zhou, Z. Xia. Bidirectional augmented reality 3D signage enabled by a metagrating waveguide. ACS Photonics, 10, 4259-4266(2023).

[9] X. Weng, J. Feng, A. Perry. Non-line-of-sight full-stokes polarimetric imaging with solution-processed metagratings and shallow neural networks. ACS Photonics, 10, 2570-2579(2023).

[10] F. Uleman, V. Neder, A. Cordaro. Resonant metagratings for spectral and angular control of light for colored rooftop photovoltaics. ACS Appl. Energy Mater., 3, 3150-3156(2020).

[11] E. Khaidarov, H. Hao, R. Paniagua-Dominguez. Asymmetric nanoantennas for ultrahigh angle broadband visible light bending. Nano Lett., 17, 6267-6272(2017).

[12] J. Shi, H. Gao, X. Jia. All-dielectric tunable terahertz metagrating for diffraction control. ACS Appl. Mater. Interfaces, 14, 55174-55182(2022).

[13] X. Zang, H. Ding, Y. Intaravanne. A multi-foci metalens with polarization-rotated focal points. Laser Photonics Rev., 13, 1900182(2019).

[14] H. Xu, Y. Shi. Silicon-waveguide-integrated high-quality metagrating supporting bound state in the continuum. Laser Photonics Rev., 14, 1900430(2020).

[15] Z. L. Deng, F. J. Li, H. Li. Extreme diffraction control in metagratings leveraging bound states in the continuum and exceptional points. Laser Photonics Rev., 16, 2100617(2022).

[16] Z. Fan, M. R. Shcherbakov, M. Allen. Perfect diffraction with multiresonant bianisotropic metagratings. ACS Photonics, 5, 4303-4311(2018).

[17] R. Gao, M. D. Kelzenberg, Y. Kim. Optical characterization of silicon nitride metagrating-based lightsails for self-stabilization. ACS Photonics, 9, 1965-1972(2022).

[18] Q. Liu, Z. Liu, X. Ma. Incomplete phase metasurface for wavefront reconstruction. ACS Photonics, 10, 2563-2569(2023).

[19] F. Ghorbani, S. Beyraghi, J. Shabanpour. Deep neural network-based automatic metasurface design with a wide frequency range. Sci. Rep., 11, 7102(2021).

[20] T. Zhan, Q.-S. Liu, Y.-J. Sun. A general machine learning-based approach for inverse design of one-dimensional photonic crystals toward targeted visible light reflection spectrum. Opt. Commun., 510, 127920(2022).

[21] J. Baxter, A. C. Lesina, J.-M. Guay. Plasmonic colours predicted by deep learning. Sci. Rep., 9, 8074(2019).

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

[23] D. Gostimirovic, W. N. Ye. Automating photonic design with machine learning. IEEE 15th International Conference on Group IV Photonics (GFP), 1-2(2018).

[24] T. Asano, S. Noda. Optimization of photonic crystal nanocavities based on deep learning. Opt. Express, 26, 32704-32716(2018).

[25] A. da Silva Ferreira, G. N. Malheiros-Silveira, H. E. Hernandez-Figueroa. Computing optical properties of photonic crystals by using multilayer perceptron and extreme learning machine. J. Lightwave Technol., 36, 4066-4073(2018).

[26] A. Tittl, A. John-Herpin, A. Leitis. Metasurface-based molecular biosensing aided by artificial intelligence. Angew. Chem.-Int. Edit., 58, 14810-14822(2019).

[27] C. Barth, C. Becker. Machine learning classification for field distributions of photonic modes. Commun. Phys., 1, 58(2018).

[28] B. Karanov, M. Chagnon, F. Thouin. End-to-end deep learning of optical fiber communications. J. Lightwave Technol., 36, 4843-4855(2018).

[29] T. A. Eriksson, H. Bulow, A. Leven. Applying neural networks in optical communication systems: possible pitfalls. IEEE Photonics Technol. Lett., 29, 2091-2094(2017).

[30] Z. Liu, D. Zhu, S. P. Rodrigues. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018).

[31] Y. Luo, D. Mengu, N. T. Yardimci. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019).

[32] 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).

[33] S. An, C. Fowler, B. Zheng. A deep learning approach for objective-driven all-dielectric metasurface design. ACS Photonics, 6, 3196-3207(2019).

[34] F. Wang, G. Geng, X. Wang. Visible achromatic metalens design based on artificial neural network. Adv. Opt. Mater., 10, 2101842(2022).

[35] J. Jiang, J. A. Fan. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett., 19, 5366-5372(2019).

[36] S. An, B. Zheng, M. Y. Shalaginov. Deep convolutional neural networks to predict mutual coupling effects in metasurfaces. Adv. Opt. Mater., 10, 2102113(2022).

[37] C.-H. Lin, Y.-S. Chen, J.-T. Lin. Automatic inverse design of high-performance beam-steering metasurfaces via genetic-type tree optimization. Nano Lett., 21, 4981-4989(2021).

[38] W. Ma, Y. Xu, B. Xiong. Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater., 34, 2110022(2022).

[39] J. Shi, M. Li, L. Tang. All-dielectric integrated meta-antenna operating in 6G terahertz communication window. Small, 20, 2308958(2024).

[40] J. Liu, X. Fang, F. He. Directional conversion of a THz propagating wave into surface waves in deformable metagratings. Opt. Express, 29, 21749-21762(2021).

[41] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

[42] J. Peurifoy, Y. Shen, L. Jing. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv., 4, eaar4206(2018).

[43] K. Kojima, B. Wang, U. Kamilov. Acceleration of FDTD-based inverse design using a neural network approach. Advanced Photonics, ITu1A.4(2017).

[44] Z. Zhang, S. Rao, X. Zhang. Performance prediction of switched reluctance motor using improved generalized regression neural networks for design optimization. CES Trans. Electr. Syst., 2, 371-376(2018).

[45] T. Zhang, J. Wang, Q. Liu. Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks. Photonics Res., 7, 368-380(2019).

[46] S. A. Yoon, S. H. Cha, S. W. Jun. Identifying different types of microorganisms with terahertz spectroscopy. Biomed. Opt. Express, 11, 406-416(2020).

[47] M. T. Alula, S. Krishnan, N. R. Hendricks. Identification and quantitation of pathogenic bacteria via in-situ formation of silver nanoparticles on cell walls, and their detection via SERS. Microchim. Acta, 184, 219-227(2017).

[48] A. Niemz, T. M. Ferguson, D. S. Boyle. Point-of-care nucleic acid testing for infectious diseases. Trends Biotechnol., 29, 240-250(2011).

[49] M. Seo, H. R. Park. Terahertz biochemical molecule-specific sensors. Adv. Opt. Mater., 8, 1900662(2020).

[50] E. Gil-Santos, J. J. Ruz, O. Malvar. Optomechanical detection of vibration modes of a single bacterium. Nat. Nanotechnol, 15, 469-474(2020).

[51] W. J. Yu, J. Shi, G. R. Huang. THz-ATR spectroscopy integrated with species recognition based on multi-classifier voting for automated clinical microbial identification. Biosensors, 12, 378(2022).

[52] S. M. Yoo, S. Y. Lee. Optical biosensors for the detection of pathogenic microorganisms. Trends Biotechnol., 34, 7-25(2016).

[53] X. Yang, K. Yang, Y. Luo. Terahertz spectroscopy for bacterial detection: opportunities and challenges. Appl. Microbiol. Biotechnol., 100, 5289-5299(2016).