• Chinese Journal of Lasers
  • Vol. 50, Issue 11, 1101007 (2023)
Yihao Luo1, Jun Zhang2, Shiyin Du2, Qiuquan Yan2, Zeyu Zhao2, Zilong Tao2, Tong Zhou1, and Tian Jiang3、*
Author Affiliations
  • 1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, Hunan, China
  • 2Institute for Quantum Information and the State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, Hunan, China
  • 3Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, Hunan, China
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    DOI: 10.3788/CJL230540 Cite this Article Set citation alerts
    Yihao Luo, Jun Zhang, Shiyin Du, Qiuquan Yan, Zeyu Zhao, Zilong Tao, Tong Zhou, Tian Jiang. Research Progress in Metamaterial Design and Fiber Beam Control Based on Deep Learning[J]. Chinese Journal of Lasers, 2023, 50(11): 1101007 Copy Citation Text show less
    References

    [1] Akyurtlu A, Werner D H. A novel dispersive FDTD formulation for modeling transient propagation in chiral metamaterials[J]. IEEE Transactions on Antennas and Propagation, 52, 2267-2276(2004).

    [2] Hao Y, Mittra R[M]. FDTD modeling of metamaterials: theory and applications(2009).

    [3] Polycarpou A C. Introduction to the finite element method in electromagnetics[J]. Synthesis Lectures on Computational Electromagnetics, 1, 1-126(2006).

    [4] Moharam M G, Gaylord T K. Rigorous coupled-wave analysis of planar-grating diffraction[J]. Journal of the Optical Society of America, 71, 811-818(1981).

    [5] Tao Z L. Prediction and design of two-dimensional chiral metamaterials based on artificial neural network algorithm[D](2020).

    [6] Puzyrev V. Deep learning electromagnetic inversion with convolutional neural networks[J]. Geophysical Journal International, 218, 817-832(2019).

    [7] An Y, Huang L J, Li J et al. Learning to decompose the modes in few-mode fibers with deep convolutional neural network[J]. Optics Express, 27, 10127-10137(2019).

    [8] Dong Y, Wu C H, Zhang C et al. Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride[J]. NPJ Computational Materials, 5, 26(2019).

    [9] Li X Z, Shu J, Gu W H et al. Deep neural network for plasmonic sensor modeling[J]. Optical Materials Express, 9, 3857-3862(2019).

    [10] Kiarashinejad Y, Abdollahramezani S, Adibi A. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures[J]. NPJ Computational Materials, 6, 12(2020).

    [11] Kiarashinejad Y, Zandehshahvar M, Abdollahramezani S et al. Knowledge discovery in nanophotonics using geometric deep learning[J]. Advanced Intelligent Systems, 2, 1900132(2020).

    [12] Xiong J K, Shen J Q, Gao Y A et al. Real-time on-demand design of circuit-analog plasmonic stack metamaterials by divide-and-conquer deep learning[J]. Laser & Photonics Reviews, 17, 2100738(2023).

    [13] Chen W, Gao Y A, Li Y Y et al. Broadband solar metamaterial absorbers empowered by transformer-based deep learning[J]. Advanced Science, 2206718(2023).

    [14] Chen Y S, Zhu J F, Xie Y N et al. Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network[J]. Nanoscale, 11, 9749-9755(2019).

    [15] Rajaraman G, Sood K, Anbazhagan S. A novel method to compute resonant frequency of metamaterial based patch antennas using neural networks[J]. International Journal for Research in Applied Science & Engineering Technology, 4, 321-325(2016).

    [16] Peurifoy J, Shen Y C, Jing L et al. Nanophotonic particle simulation and inverse design using artificial neural networks[J]. Science Advances, 4, eaar4206(2018).

    [17] Inampudi S, Mosallaei H. Neural network based design of metagratings[J]. Applied Physics Letters, 112, 241102(2018).

    [18] Sajedian I, Kim J, Rho J. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks[J]. Microsystems & Nanoengineering, 5, 27(2019).

    [19] Qu Y R, Jing L, Shen Y C et al. Migrating knowledge between physical scenarios based on artificial neural networks[J]. ACS Photonics, 6, 1168-1174(2019).

    [20] Malkiel I, Mrejen M, Nagler A et al. Plasmonic nanostructure design and characterization via deep learning[J]. Light: Science & Applications, 7, 60(2018).

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

    [22] Wiecha P R, Muskens O L. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures[J]. Nano Letters, 20, 329-338(2020).

    [23] Chang H X, Chang Q, Xi J C et al. First experimental demonstration of coherent beam combining of more than 100 beams[J]. Photonics Research, 8, 1943-1948(2020).

    [24] Yan Q Q, Deng Q H, Zhang J et al. Low-latency deep-reinforcement learning algorithm for ultrafast fiber lasers[J]. Photonics Research, 9, 1493-1501(2021).

    [25] Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future[J]. Multimedia Tools and Applications, 80, 8091-8126(2021).

    [26] Ang K H, Chong G, Li Y. PID control system analysis, design, and technology[J]. IEEE Transactions on Control Systems Technology, 13, 559-576(2005).

    [27] Zhou P, Liu Z J, Ma Y X et al. Bandwidth analysis and improvement of the beam phasing of fiber amplifiers using the stochastic parallel gradient descent algorithm[J]. Optics & Laser Technology, 42, 1059-1065(2010).

    [28] Nguyen T T, Nguyen N D, Nahavandi S. Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications[J]. IEEE Transactions on Cybernetics, 50, 3826-3839(2020).

    [29] Zhu S Q, Yu T, Xu T et al. Intelligent computing: the latest advances, challenges, and future[J]. Intelligent Computing, 2, 6(2023).

    [30] Goodfellow I, Bengio Y, Courville A[M]. Deep learning(2016).

    [31] McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biology, 5, 115-133(1943).

    [32] LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series[M]. Arbib M A. The handbook of brain theory and neural networks(1995).

    [33] Graves A. Long short-term memory[M]. Graves A. Supervised sequence labelling with recurrent neural networks. Studies in computational intelligence, 385, 37-45(2012).

    [34] Du S Y, You J, Tang Y H et al. Achieving efficient inverse design of low-dimensional heterostructures based on a vigorous scalable multi-task learning network[J]. Optics Express, 29, 19727-19742(2021).

    [35] Du S Y, You J E, Zhang J et al. Expedited circular dichroism prediction and engineering in two-dimensional diffractive chiral metamaterials leveraging a powerful model-agnostic data enhancement algorithm[J]. Nanophotonics, 10, 1155-1168(2021).

    [36] Tao Z L, Zhang J, You J E et al. Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials[J]. Nanophotonics, 9, 2945-2956(2020).

    [37] Hadji I, Wildes R P. What do we understand about convolutional networks?[EB/OL]. https://arxiv.org/abs/1803.08834

    [38] Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks[EB/OL]. https://arxiv.org/abs/1211.5063

    [39] LeCun Y, Fogelman-Soulié F. Modèlesconnexionnistes de l'apprentissage[J]. Intellectica Revue De L’Association Pour La Recherche Cognitive, 2, 114-143(1987).

    [40] Bourlard H, Kamp Y. Auto-association by multilayer perceptrons and singular value decomposition[J]. Biological Cybernetics, 59, 291-294(1988).

    [41] Hinton G E, Zemel R S. Autoencoders, minimum description length and Helmholtz free energy[C], 3-10(1993).

    [42] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[J]. Communications of the ACM, 63, 139-144(2020).

    [43] Sutton R S, Barto A G[M]. Reinforcement learning: an introduction(2018).

    [44] van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double Q-learning[C], 2094-2100(2016).

    [45] Lai J, Wang X D, Xiang Q et al. Review on autoencoder and its application[J]. Journal on Communications, 42, 218-230(2021).

    [46] Wang Z L, Zhang B W. Survey of generative adversarial network[J]. Chinese Journal of Network and Information Security, 7, 68-85(2021).

    [47] Russell S J, Norvig P, Davis E[M]. Artificial intelligence: a modern approach(2010).

    [48] Ma J H, Piao Z, Huang S et al. Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation[J]. Photonics Research, 9, B45-B56(2021).

    [49] Mnih V, Kavukcuoglu K, Silver D et al. Human-level control through deep reinforcement learning[J]. Nature, 518, 529-533(2015).

    [50] Lillicrap T P, Hunt J J, Pritzel A et al. Continuous control with deep reinforcement learning[EB/OL]. https://arxiv.org/abs/1509.02971

    [51] Mnih V, Kavukcuoglu K, Silver D et al. Playing Atari with deep reinforcement learning[EB/OL]. https://arxiv.org/abs/1312.5602

    [52] Yang S M, Shan Z, Ding Y et al. Survey of research on deep reinforcement learning[J]. Computer Engineering, 47, 19-29(2021).

    [53] Bottou L. Stochastic gradient descent tricks[M]. Montavon G, Orr G B, Müller K B. Neural networks: tricks of the trade. Lecture notes in computer science, 7700, 421-436(2012).

    [54] Polyak B T. Some methods of speeding up the convergence of iteration methods[J]. USSR Computational Mathematics and Mathematical Physics, 4, 1-17(1964).

    [55] Sutskever I, Martens J, Dahl G et al. On the importance of initialization and momentum in deep learning[C], 2176-2184(2013).

    [56] Duchi J C, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization[J]. Journal of Machine Learning Research, 12, 2121-2159(2011).

    [57] Hinton G. Graduate summer school: deep learning, feature learning[EB/OL]. https://www.ipam.ucla.edu/schedule.aspx?pc=gss2012

    [58] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

    [59] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 323, 533-536(1986).

    [60] Veselago V G. The electrodynamics of substances with simultaneously negative values of ε and μ[J]. Soviet Physics Uspekhi, 10, 509-514(1968).

    [61] Seddon N, Bearpark T. Observation of the inverse Doppler effect[J]. Science, 302, 1537-1540(2003).

    [62] Luo C Y, Ibanescu M, Johnson S G et al. Cerenkov radiation in photonic crystals[J]. Science, 299, 368-371(2003).

    [63] Veselago V G, Narimanov E E. The left hand of brightness: past, present and future of negative index materials[J]. Nature Materials, 5, 759-762(2006).

    [64] Pendry J B, Smith D R. Reversing light with negative refraction[J]. Physics Today, 57, 37-43(2004).

    [65] Pendry J B, Holden A J, Stewart W J et al. Extremely low frequency plasmons in metallic mesostructures[J]. Physical Review Letters, 76, 4773-4776(1996).

    [66] Pendry J B, Holden A J, Robbins D J et al. Magnetism from conductors and enhanced nonlinear phenomena[J]. IEEE Transactions on Microwave Theory and Techniques, 47, 2075-2084(1999).

    [67] Smith D R, Padilla W J, Vier D C et al. Composite medium with simultaneously negative permeability and permittivity[J]. Physical Review Letters, 84, 4184-4187(2000).

    [68] Hu Y Z, Jiang T A, Sun H et al. Ultrafast frequency shift of electromagnetically induced transparency in terahertz metaphotonic devices[J]. Laser & Photonics Reviews, 14, 1900338(2020).

    [69] Lee Y Y, Kim R M, Im S W et al. Plasmonic metamaterials for chiral sensing applications[J]. Nanoscale, 12, 58-66(2020).

    [70] Yao K, Zheng Y B. Near-ultraviolet dielectric metasurfaces: from surface-enhanced circular dichroism spectroscopy to polarization-preserving mirrors[J]. The Journal of Physical Chemistry C, 123, 11814-11822(2019).

    [71] Kogelnik H. Coupled wave theory for thick hologram gratings[J]. The Bell System Technical Journal, 48, 2909-2947(1969).

    [72] Lee W, Degertekin F L. Rigorous coupled-wave analysis for multilayered grating structures[J]. Proceedings of SPIE, 4987, 264-273(2003).

    [73] Moharam M G, Gaylord T K. Rigorous coupled-wave analysis of grating diffraction: E-mode polarization and losses[J]. Journal of the Optical Society of America, 73, 451-455(1983).

    [74] Peng S, Morris G M. Efficient implementation of rigorous coupled-wave analysis for surface-relief gratings[J]. Journal of the Optical Society of America A, 12, 1087-1096(1995).

    [75] Weismann M, Gallagher D F G, Panoiu N C. Accurate near-field calculation in the rigorous coupled-wave analysis method[J]. Journal of Optics, 17, 125612(2015).

    [76] Li Y. Design and analysis of infrared metamaterials based on RCWA method[D](2020).

    [77] Shao T Y, Gu J Q, Shi W Q. Automated design study of guided-mode resonance filters working at terahertz frequencies[J]. Chinese Journal of Lasers, 48, 2014001(2021).

    [78] Xu D D. Perfect absorption mechanism and dynamic radiation modulation of metamaterials in mid-infrared band[D](2022).

    [79] Chen D W. Strict coupled wave analysis method in diffractive optics[D](2004).

    [80] Yee K E. Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media[J]. IEEE Transactions on Antennas and Propagation, 14, 302-307(1966).

    [81] Niu K K. Improvement of finite-difference time-domain method and its application in multiple physical fields[D](2019).

    [82] Niu K K, Xu H, Zhu D et al. Recent progress and future development of metamaterial and gain material[J]. Journal of Anhui University (Natural Science Edition), 41, 24-33(2017).

    [83] Molesky S, Lin Z, Piggott A Y et al. Inverse design in nanophotonics[J]. Nature Photonics, 12, 659-670(2018).

    [84] Yu Z J, Cui H R, Sun X K. Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint[J]. Optics Letters, 42, 3093-3096(2017).

    [85] Ma L F, Li J, Liu Z H et al. Intelligent algorithms: new avenues for designing nanophotonic devices[J]. Chinese Optics Letters, 19, 011301(2021).

    [86] Wang X Y, Wu T Y, Dong C et al. Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence[J]. Photonics Research, 9, B9-B17(2021).

    [87] Zhen Z, Qian C, Jia Y T et al. Realizing transmitted metasurface cloak by a tandem neural network[J]. Photonics Research, 9, B229-B235(2021).

    [88] Liu C, Yu W M, Ma Q et al. Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network[J]. Photonics Research, 9, B159-B167(2021).

    [89] Peurifoy J, Shen Y C, Jing L et al. Nanophotonic particle simulation and inverse design using artificial neural networks[J]. Proceedings of SPIE, 10526, 1052607(2018).

    [90] Liu D J, Tan Y X, Khoram E et al. Training deep neural networks for the inverse design of nanophotonic structures[J]. ACS Photonics, 5, 1365-1369(2018).

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

    [92] Zhu R C, Qiu T S, Wang J F et al. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning[J]. Nature Communications, 12, 2974(2021).

    [93] Jiang J Q, Sell D, Hoyer S et al. Free-form diffractive metagrating design based on generative adversarial networks[J]. ACS Nano, 13, 8872-8878(2019).

    [94] Sajedian I, Lee H, Rho J. Double-deep Q-learning to increase the efficiency of metasurface holograms[J]. Scientific Reports, 9, 10899(2019).

    [95] Zhao Z Y, You J, Zhang J et al. Data-enhanced deep greedy optimization algorithm for the on-demand inverse design of TMDC-cavity heterojunctions[J]. Nanomaterials, 12, 2976(2022).

    [96] Zhao X, Li T, Liu Y et al. Polarization-multiplexed, dual-comb all-fiber mode-locked laser[J]. Photonics Research, 6, 853-857(2018).

    [97] Yin K, Li Y M, Wang Y B et al. Self-starting all-fiber PM Er:laser mode locked by a biased nonlinear amplifying loop mirror[J]. Chinese Physics B, 28, 124203(2019).

    [98] Zou J H, Dong C C, Wang H J et al. Towards visible-wavelength passively mode-locked lasers in all-fibre format[J]. Light: Science & Applications, 9, 61(2020).

    [99] Li W S, Zhu C H, Rong X F et al. Bidirectional red-light passively Q-switched all-fiber ring lasers with carbon nanotube saturable absorber[J]. Journal of Lightwave Technology, 36, 2694-2701(2018).

    [100] Liu J, Wu J D, Chen H L et al. Short-pulsed Raman fiber laser and its dynamics[J]. Science China Physics, Mechanics & Astronomy, 64, 214201(2021).

    [101] Huang D M, Shang C, Li F et al. Discrete Fourier domain harmonically mode locked laser by mode hopping modulation[C](2019).

    [102] Winters D G, Kirchner M S, Backus S J et al. Electronic initiation and optimization of nonlinear polarization evolution mode-locking in a fiber laser[J]. Optics Express, 25, 33216-33225(2017).

    [103] Pu G Q, Yi L L, Zhang L et al. Genetic algorithm-based fast real-time automatic mode-locked fiber laser[J]. IEEE Photonics Technology Letters, 32, 7-10(2020).

    [104] Pu G Q, Yi L L, Zhang L et al. Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis[J]. Light: Science & Applications, 9, 13(2020).

    [105] Fu X, Kutz J N. High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm[J]. Optics Express, 21, 6526-6537(2013).

    [106] Kokhanovskiy A, Bednyakova A, Kuprikov E et al. Machine learning-based pulse characterization in figure-eight mode-locked lasers[J]. Optics Letters, 44, 3410-3413(2019).

    [107] Hellwig T, Walbaum T, Groß P et al. Automated characterization and alignment of passively mode-locked fiber lasers based on nonlinear polarization rotation[J]. Applied Physics B, 101, 565-570(2010).

    [108] Brunton S L, Fu X, Kutz J N. Extremum-seeking control of a mode-locked laser[J]. IEEE Journal of Quantum Electronics, 49, 852-861(2013).

    [109] Kutz J N, Brunton S L. Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics[J]. Nanophotonics, 4, 459-471(2015).

    [110] Meng F C, Dudley J M. Toward a self-driving ultrafast fiber laser[J]. Light: Science & Applications, 9, 26(2020).

    [111] Kokhanovskiy A, Shevelev A, Serebrennikov K et al. A deep reinforcement learning algorithm for smart control of hysteresis phenomena in a mode-locked fiber laser[J]. Photonics, 9, 921(2022).

    [112] Woodward R I, Kelleher E J R. Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm[J]. Scientific Reports, 6, 37616(2016).

    [113] Pu G Q, Yi L L, Zhang L et al. Intelligent programmable mode-locked fiber laser with a human-like algorithm[J]. Optica, 6, 362-369(2019).

    [114] Pu G Q, Liu R M, Luo C et al. Intelligent single-cavity dual-comb source with fast locking[J]. Journal of Lightwave Technology, 41, 593-598(2023).

    [115] Sun C, Kaiser E, Brunton S L et al. Deep reinforcement learning for optical systems: a case study of mode-locked lasers[J]. Machine Learning: Science and Technology, 1, 045013(2020).

    [116] Li Z, Yang S S, Xiao Q et al. Deep reinforcement with spectrum series learning control for a mode-locked fiber laser[J]. Photonics Research, 10, 1491-1500(2022).

    [117] Luo S Y, Tang X A, Geng X A et al. Ultrafast true-green Ho:ZBLAN fiber laser inspired by the TD3 AI algorithm[J]. Optics Letters, 47, 5881-5884(2022).

    [118] Andral U, Fodil R S, Amrani F et al. Fiber laser mode locked through an evolutionary algorithm[J]. Optica, 2, 275-278(2015).

    [119] Zhou P, Liu Z J, Wang X L et al. Coherent beam combination of two-dimensional high power fiber amplifier array using stochastic parallel gradient descent algorithm[J]. Applied Physics Letters, 94, 231106(2009).

    [120] Hou T Y, An Y, Chang Q et al. Deep learning-based phase control method for coherent beam combining and its application in generating orbital angular momentum beams[EB/OL]. https://arxiv.org/abs/1903.03983

    [121] Tünnermann H, Shirakawa A. Deep reinforcement learning for coherent beam combining applications[J]. Optics Express, 27, 24223-24230(2019).

    [122] Hirose A. Applications of complex-valued neural networks to coherent optical computing using phase-sensitive detection scheme[J]. Information Sciences-Applications, 2, 103-117(1994).

    [123] Zhang J, Yan Q Q, Liu H Z et al. Coherent optical neuron control based on reinforcement learning[J]. Optics Letters, 48, 1084-1087(2023).

    Yihao Luo, Jun Zhang, Shiyin Du, Qiuquan Yan, Zeyu Zhao, Zilong Tao, Tong Zhou, Tian Jiang. Research Progress in Metamaterial Design and Fiber Beam Control Based on Deep Learning[J]. Chinese Journal of Lasers, 2023, 50(11): 1101007
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