[1] M. B. Ross, C. A. Mirkin, G. C. Schatz. Optical properties of one-, two-, and three-dimensional arrays of plasmonic nanostructures. J. Phys. Chem. C, 120, 816(2016).

[2] M. Mayer, M. J. Schnepf, T. A. König, A. Fery. Colloidal self-assembly concepts for plasmonic metasurfaces. Adv. Opt. Mater., 7, 1800564(2019).

[3] C. Valsecchi, A. G. Brolo. Periodic metallic nanostructures as plasmonic chemical sensors. Langmuir, 29, 5638(2013).

[4] B. Špačková, P. Wrobel, M. Bocková, J. Homola. Optical biosensors based on plasmonic nanostructures: a review. Proc. IEEE, 104, 2380(2016).

[5] Y. Zhang, Z. Shuai, H. Zhou, Z. Luo, B. Liu, Y. Zhang, L. Zhang, S. Chen, J. Chao, L. Weng. Single-molecule analysis of microRNA and logic operations using a smart plasmonic nanobiosensor. J. Am. Chem. Soc., 140, 3988(2018).

[6] D. Lu, Z. Liu. Hyperlenses and metalenses for far-field super-resolution imaging. Nat. Commun., 3, 1205(2012).

[7] A. Bezryadina, J. Zhao, Y. Xia, X. Zhang, Z. Liu. High spatiotemporal resolution imaging with localized plasmonic structured illumination microscopy. ACS Nano, 12, 8248(2018).

[8] D. L. Mack, E. Cortés, V. Giannini, P. Török, T. Roschuk, S. A. Maier. Decoupling absorption and emission processes in super-resolution localization of emitters in a plasmonic hotspot. Nat. Commun., 8, 1(2017).

[9] S. J. Tan, L. Zhang, D. Zhu, X. M. Goh, Y. M. Wang, K. Kumar, C.-W. Qiu, J. K. Yang. Plasmonic color palettes for photorealistic printing with aluminum nanostructures. Nano Lett., 14, 4023(2014).

[10] K. Kumar, H. Duan, R. S. Hegde, S. C. Koh, J. N. Wei, J. K. Yang. Printing colour at the optical diffraction limit. Nat. Nanotechnol., 7, 557(2012).

[11] X. Duan, S. Kamin, N. Liu. Dynamic plasmonic colour display. Nat. Commun., 8, 14606(2017).

[12] S. D. Rezaei, R. J. Hong Ng, Z. Dong, J. Ho, E. H. Koay, S. Ramakrishna, J. K. Yang. Wide-gamut plasmonic color palettes with constant subwavelength resolution. ACS Nano, 13, 3580(2019).

[13] P. R. Wiecha, A. Lecestre, N. Mallet, G. Larrieu. Pushing the limits of optical information storage using deep learning. Nat. Nanotechnol., 14, 237(2019).

[14] L. Gao, Y. Zhang, H. Zhang, S. Doshay, X. Xie, H. Luo, D. Shah, Y. Shi, S. Xu, H. Fang. Optics and nonlinear buckling mechanics in large-area, highly stretchable arrays of plasmonic nanostructures. ACS Nano, 9, 5968(2015).

[15] L. Gao, K. Shigeta, A. Vazquez-Guardado, C. J. Progler, G. R. Bogart, J. A. Rogers, D. Chanda. Nanoimprinting techniques for large-area three-dimensional negative index metamaterials with operation in the visible and telecom bands. ACS Nano, 8, 5535(2014).

[16] L. Gao, Y. Kim, A. Vazquez-Guardado, K. Shigeta, S. Hartanto, D. Franklin, C. J. Progler, G. R. Bogart, J. A. Rogers, D. Chanda. Materials selections and growth conditions for large-area, multilayered, visible negative index metamaterials formed by nanotransfer printing. Adv. Opt. Mater., 2, 256(2014).

[17] G. Yoon, K. Kim, S.-U. Kim, S. Han, H. Lee, J. Rho. Printable nanocomposite metalens for high-contrast near-infrared imaging. ACS Nano, 15, 698(2021).

[18] W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, Y. Liu. Deep learning for the design of photonic structures. Nat. Photon., 15, 77(2021).

[19] S. So, J. Rho. Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics, 8, 1255(2019).

[20] S. So, T. Badloe, J. Noh, J. Bravo-Abad, J. Rho. Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9, 1041(2020).

[21] T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, Y. Zhou, K. Xu. Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks. Photonics Res., 7, 368(2019).

[22] D. Liu, Y. Tan, E. Khoram, Z. Yu. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics, 5, 1365(2018).

[23] S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, A. W. Rodriguez. Inverse design in nanophotonics. Nat. Photon., 12, 659(2018).

[24] A. Movsesyan, L. V. Besteiro, Z. Wang, A. O. Govorov. Mie sensing with neural networks: recognition of nano-object parameters, the invisibility point, and restricted models. Adv. Theor. Simul., 5, 2100369(2022).

[25] X. Cao, Y. Xiao, Q. Dong, S. Zhang, J. Wang, L. Wang, L. Gao. Tuning metasurface dimensions by soft nanoimprint lithography and reactive ion etching. Adv. Photon. Res., 2200127(2022).

[26] Q. Wu, X. Li, W. Wang, Q. Dong, Y. Xiao, X. Cao, L. Wang, L. Gao. Comparison of different neural network architectures for plasmonic inverse design. ACS Omega, 6, 23076(2021).

[27] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

[28] S. So, J. Mun, J. Rho. Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles. ACS Appl. Mater. Interfaces, 11, 24264(2019).

[29] C. Lee, J. Na, K. Park, H. Yu, J. Kim, K. Choi, D. Park, S. Park, J. Rho, S. Lee. Development of artificial neural network system to recommend process conditions of injection molding for various geometries. Adv. Intell. Syst., 2, 2000037(2020).