Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

Fu Feng; Dewang Huo; Ziyang Zhang; Yijie Lou; Shengyao Wang; Zhijuan Gu; Dong-Sheng Liu; Xinhui Duan; Daqian Wang; Xiaowei Liu; Ji Qi; Shaoliang Yu; Qingyang Du; Guangyong Chen; Cuicui Lu; Yu Yu; Xifeng Ren; Xiaocong Yuan

doi:10.1117/1.AP.7.2.024001

Advanced Photonics, Volume. 7, Issue 2, 024001(2025)

Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

Fu Feng1,†... Dewang Huo1,2, Ziyang Zhang1, Yijie Lou1, Shengyao Wang3, Zhijuan Gu4, Dong-Sheng Liu5,6, Xinhui Duan1, Daqian Wang1, Xiaowei Liu1, Ji Qi1,*, Shaoliang Yu1, Qingyang Du1,*, Guangyong Chen7,*, Cuicui Lu3,*, Yu Yu4,*, Xifeng Ren5,6,* and Xiaocong Yuan1,* |Show fewer author(s)

Author Affiliations

¹Zhejiang Lab, Research Center for Frontier Fundamental Studies, Hangzhou, China

²Westlake Institute for Optoelectronics, Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization, Hangzhou, China

³Beijing Institute of Technology, School of Physics, Center for Interdisciplinary Science of Optical Quantum and NEMS Integration, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems, Beijing, China

⁴Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Wuhan, China

⁵University of Science and Technology of China, CAS Key Laboratory of Quantum Information, Hefei, China

⁶University of Science and Technology of China, CAS Center for Excellence in Quantum Information and Quantum Physics, Hefei, China

⁷Zhejiang Lab, Research Center for Life Sciences Computing, Hangzhou, China

show less

References(567)

[1] W. S. McCulloch, W. H. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133(1943). https://doi.org/10.1007/BF02478259

[2] A. d’Avila Garcez, L. C. Lamb. Neurosymbolic AI: the 3rd wave. Artif. Intell. Rev., 56, 12387-12406(2020). https://doi.org/10.1007/s10462-023-10448-w

[3] M. Magris, A. Iosifidis. Bayesian learning for neural networks: an algorithmic survey. Artif. Intell. Rev., 56, 11773-11823(2023). https://doi.org/10.1007/s10462-023-10443-1

[4] S. S. Keerthi et al. Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput., 13, 637-649(2001). https://doi.org/10.1162/089976601300014493

[5] T. Hastie et al. Multi-class AdaBoost. Stat. Interface, 2, 349-360(2009). https://doi.org/10.4310/SII.2009.v2.n3.a8

[6] J. W. Catto et al. Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks. Clin. Cancer Res., 9, 4172-4177(2003).

[7] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks(2012).

[8] Y. Li. Research and application of deep learning in image recognition, 994-999(2022). https://doi.org/10.1109/ICPECA53709.2022.9718847

[9] K. He et al. Deep residual learning for image recognition, 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[10] D. W. Otter, J. R. Medina, J. K. Kalita. A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Networks Learn. Syst., 32, 604-624(2020). https://doi.org/10.1109/TNNLS.2020.2979670

[11] Z. Zhang et al. Deep learning for environmentally robust speech recognition: an overview of recent developments. ACM Trans. Intell. Syst. Technol., 9, 49(2018). https://doi.org/10.1145/3178115

[12] L. Floridi, M. J. M. Chiriatti. GPT-3: its nature, scope, limits, and consequences. Minds and Mach., 30, 681-694(2020).

[13] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018). https://doi.org/10.1126/science.aat8084

[14] W. Ma et al. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2021). https://doi.org/10.1038/s41566-020-0685-y

[15] A. Kumar et al. Artificial intelligence techniques for the photovoltaic system: a systematic review and analysis for evaluation and benchmarking. Arch. Comput. Methods Eng., 31, 4429-4453(2024). https://doi.org/10.1007/s11831-024-10125-3

[16] A. Esteva et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115-118(2017). https://doi.org/10.1038/nature21056

[17] D. George, E. A. Huerta. Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data. Phys. Lett. B, 778, 64-70(2018). https://doi.org/10.1016/j.physletb.2017.12.053

[18] T. Yin, X. Zhou, P. Krahenbuhl. Center-based 3D object detection and tracking, 11779-11788(2021). https://doi.org/10.1109/CVPR46437.2021.01161

[19] O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015). https://doi.org/10.1007/978-3-319-24574-4_28

[20] P. Wang, B. Bayram, E. Sertel. A comprehensive review on deep learning based remote sensing image super-resolution methods. Earth Sci. Rev., 232, 104110(2022). https://doi.org/10.1016/j.earscirev.2022.104110

[21] Y. Ghasemi et al. Deep learning-based object detection in augmented reality: a systematic review. Comput. Ind., 139, 103661(2022). https://doi.org/10.1016/j.compind.2022.103661

[22] F. N. Khan et al. An optical communication’s perspective on machine learning and its applications. J. Lightwave Technol., 37, 493-516(2019). https://doi.org/10.1109/JLT.2019.2897313

[23] Z. Tian et al. Prediction of overlying rock deformation based on LSTM in optical fiber sensor monitoring, 968-974(2021). https://doi.org/10.1109/QRS-C55045.2021.00146

[24] D. Yu, X. Qiao, X. Wang. Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model. Front. Energy Res., 10, 972437(2022). https://doi.org/10.3389/fenrg.2022.972437

[25] T. H. Maiman. Stimulated optical radiation in ruby. Nature, 187, 493-494(1960). https://doi.org/10.1038/187493a0

[26] K. C. Kao, G. A. Hockham. Dielectric-fibre surface waveguides for optical frequencies. Proc. Inst. of Electr. Eng., 113, 189(1966). https://doi.org/10.1049/piee.1966.0189

[27] J. Sipe et al. Analysis of second-harmonic generation at metal surfaces. Phys. Rev. B, 21, 4389-4402(1980). https://doi.org/10.1103/PhysRevB.21.4389

[28] E. Yablonovitch. Inhibited spontaneous emission in solid-state physics and electronics. Phys. Rev. Lett., 58, 2059-2062(1987). https://doi.org/10.1103/PhysRevLett.58.2059

[29] S. W. Hell, J. Wichmann. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett., 19, 780-782(1994). https://doi.org/10.1364/OL.19.000780

[30] J. Sun et al. Large-scale silicon photonic circuits for optical phased arrays. IEEE J. Sel. Top. Quantum Electron., 20, 264-278(2013). https://doi.org/10.1109/JSTQE.2013.2293316

[31] F. Kish et al. System-on-chip photonic integrated circuits. IEEE J. Sel. Top. Quantum Electron., 24, 6100120(2017). https://doi.org/10.1109/JSTQE.2017.2717863

[32] Y. Shen et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics, 11, 441-446(2017). https://doi.org/10.1038/nphoton.2017.93

[33] M. S. Kim, C. C. Guest. Opto-neural system for pattern classification(1990).

[34] C. E. Floyd. An artificial neural network for SPECT image reconstruction. IEEE Trans. Med. Imaging, 10, 485-487(1991). https://doi.org/10.1109/42.97600

[35] B. Wu et al. Real-valued optical matrix computing with simplified MZI mesh. Intell. Comput., 2, 0047(2023). https://doi.org/10.34133/icomputing.0047

[36] M. J. Heck et al. Hybrid silicon photonics for optical interconnects. IEEE J. Sel. Top. Quantum Electron., 17, 333-346(2010). https://doi.org/10.1109/JSTQE.2010.2051798

[37] C. Xiang, J. E. Bowers. Building 3D integrated circuits with electronics and photonics. Nat. Electron., 7, 422-424(2024). https://doi.org/10.1038/s41928-024-01187-z

[38] M. Milanizadeh et al. Canceling thermal cross-talk effects in photonic integrated circuits. J. Lightwave Technol., 37, 1325-1332(2019). https://doi.org/10.1109/JLT.2019.2892512

[39] J. Wu et al. Two-dimensional materials for integrated photonics: recent advances and future challenges. Small Sci., 1, 2000053(2021). https://doi.org/10.1002/smsc.202000053

[40] Y. Zheng et al. Photonic neural network fabricated on thin film lithium niobate for high-fidelity and power-efficient matrix computation. Laser Photonics Rev., 18, 2400565(2024). https://doi.org/10.1002/lpor.202400565

[41] B. Shi, N. Calabretta, R. Stabile. InP photonic integrated multi-layer neural networks: architecture and performance analysis. APL Photonics, 7, 010801(2022). https://doi.org/10.1063/5.0066350

[42] Z. Cheng et al. Device-level photonic memories and logic applications using phase-change materials. Adv. Mater., 30, 1802435(2018). https://doi.org/10.1002/adma.201802435

[43] J.-F. Song et al. Integrated photonics with programmable non-volatile memory. Sci. Rep., 6, 22616(2016). https://doi.org/10.1038/srep22616

[44] W. Shi et al. Lensless opto-electronic neural network with quantum dot nonlinear activation. Photonics Res., 12, 682-690(2024). https://doi.org/10.1364/PRJ.515349

[45] H. J. Caulfield, J. Kinser, S. K. Rogers. Optical neural networks. Proc. IEEE, 77, 1573-1583(1989). https://doi.org/10.1109/5.40669

[46] X. Sui et al. A review of optical neural networks. IEEE Access, 8, 70773-70783(2020). https://doi.org/10.1109/ACCESS.2020.2987333

[47] J. Liu et al. Research progress in optical neural networks: theory, applications and developments. PhotoniX, 2, 5(2021). https://doi.org/10.1186/s43074-021-00026-0

[48] R. Xu et al. A survey of approaches for implementing optical neural networks. Opt. Laser Technol., 136, 106787(2021). https://doi.org/10.1016/j.optlastec.2020.106787

[49] T. Yan et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019). https://doi.org/10.1103/PhysRevLett.123.023901

[50] G. Wetzstein et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020). https://doi.org/10.1038/s41586-020-2973-6

[51] J. Feldmann et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 569, 208-214(2019). https://doi.org/10.1038/s41586-019-1157-8

[52] J. Li et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photonics, 1, 046001(2019). https://doi.org/10.1117/1.AP.1.4.046001

[53] D. Mengu et al. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top. Quantum Electron., 26, 3700114(2019). https://doi.org/10.1109/JSTQE.2019.2921376

[54] F. Léonard et al. Co-design of free-space metasurface optical neuromorphic classifiers for high performance. ACS Photonics, 8, 2103-2111(021). https://doi.org/10.1021/acsphotonics.1c00526

[55] X. Xu et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021). https://doi.org/10.1038/s41586-020-03063-0

[56] T. Wang et al. An optical neural network using less than 1 photon per multiplication. Nat. Commun., 13, 123(2022). https://doi.org/10.1038/s41467-021-27774-8

[57] Y. Luo et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019). https://doi.org/10.1038/s41377-019-0223-1

[58] M. Veli et al. Terahertz pulse shaping using diffractive surfaces. Nat. Commun., 12, 37(2021). https://doi.org/10.1038/s41467-020-20268-z

[59] A. Mirhoseini et al. A graph placement methodology for fast chip design. Nature, 594, 207-212(2021). https://doi.org/10.1038/s41586-021-03544-w

[60] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015). https://doi.org/10.1038/nature14539

[61] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning representations by back-propagating errors. Nature, 323, 533-536(1986). https://doi.org/10.1038/323533a0

[62] P. J. Werbos. Backpropagation through time: what it does and how to do it. Proc. IEEE, 78, 1550-1560(1990). https://doi.org/10.1109/5.58337

[63] D. Liu et al. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics, 5, 1365-1369(2018). https://doi.org/10.1021/acsphotonics.7b01377

[64] M. Abadi et al. TensorFlow: a system for large-scale machine learning, 265-283(2016).

[65] 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). https://doi.org/10.1002/adma.201901111

[66] S. Xia et al. Deep-learning-empowered synthetic dimension dynamics: morphing of light into topological modes. Adv. Photonics, 6, 026005(2024). https://doi.org/10.1117/1.AP.6.2.026005

[67] 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, 24264-24268(2019). https://doi.org/10.1021/acsami.9b05857

[68] C. Qian et al. Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics, 14, 383-390(2020). https://doi.org/10.1038/s41566-020-0604-2

[69] P. R. Wiecha et al. Pushing the limits of optical information storage using deep learning. Nat. Nanotechnol., 14, 237-244(2019). https://doi.org/10.1038/s41565-018-0346-1

[70] C. Zhang et al. Inverse design of soliton microcomb based on genetic algorithm and deep learning. Opt. Express, 30, 44395-44407(2022). https://doi.org/10.1364/OE.471706

[71] L. Gao et al. A bidirectional deep neural network for accurate silicon color design. Adv. Mater., 31, 1905467(2019). https://doi.org/10.1002/adma.201905467

[72] D. Melati et al. Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nat. Commun., 10, 4775(2019). https://doi.org/10.1038/s41467-019-12698-1

[73] M. H. Tahersima et al. Deep neural network inverse design of integrated photonic power splitters. Sci. Rep., 9, 1368(2019). https://doi.org/10.1038/s41598-018-37952-2

[74] J. H. Han et al. Neural-network-enabled design of a chiral plasmonic nanodimer for target-specific chirality sensing. ACS Nano, 17, 2306-2317(2023). https://doi.org/10.1021/acsnano.2c08867

[75] E. Adibnia et al. Nanophotonic structure inverse design for switching application using deep learning. Sci. Rep., 14, 21094(2024). https://doi.org/10.1038/s41598-024-72125-4

[76] J. Jiang, M. Chen, J. A. Fan. Deep neural networks for the evaluation and design of photonic devices. Nat. Rev. Mater., 6, 679-700(2021). https://doi.org/10.1038/s41578-020-00260-1

[77] R. Li et al. Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities. Nanophotonics, 12, 319-334(2023). https://doi.org/10.1515/nanoph-2022-0692

[78] Y. Liu et al. A hybrid algorithm for electromagnetic optimization utilizing neural networks, 261-263(2018). https://doi.org/10.1109/EPEPS.2018.8534264

[79] A. Vallone, N. M. Estakhri, N. M. Estakhri. Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning. J. Phys.: Photonics, 5, 024002(2023). https://doi.org/10.1088/2515-7647/acc7e5

[80] S. Hemayat et al. Integrating deep convolutional surrogate solvers and particle swarm optimization for efficient inverse design of plasmonic patch nanoantennas. Nanophotonics, 13, 3963-3983(2024). https://doi.org/10.1515/nanoph-2024-0195

[81] B. Xiong et al. Deep learning design for multiwavelength infrared image sensors based on dielectric freeform metasurface. Adv. Opt. Mater., 12, 2302200(2024). https://doi.org/10.1002/adom.202302200

[82] M. Chen et al. High speed simulation and freeform optimization of nanophotonic devices with physics-augmented deep learning. ACS Photonics, 9, 3110-3123(2022). https://doi.org/10.1021/acsphotonics.2c00876

[83] Q. Zhang et al. Artificial neural networks enabled by nanophotonics. Light Sci. Appl., 8, 42(2019). https://doi.org/10.1038/s41377-019-0151-0

[84] R. S. Hegde. Deep learning: a new tool for photonic nanostructure design. Nanoscale Adv., 2, 1007-1023(2020). https://doi.org/10.1039/C9NA00656G

[85] S. So et al. Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9, 1041-1057(2020). https://doi.org/10.1515/nanoph-2019-0474

[86] 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). https://doi.org/10.1364/PRJ.417693

[87] P. R. Wiecha et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res., 9, B182-B200(2021). https://doi.org/10.1364/PRJ.415960

[88] Z. Liu et al. Tackling photonic inverse design with machine learning. Adv. Sci., 8, 2002923(2021). https://doi.org/10.1002/advs.202002923

[89] N. Wang et al. Intelligent designs in nanophotonics: from optimization towards inverse creation. PhotoniX, 2, 22(2021). https://doi.org/10.1186/s43074-021-00044-y

[90] L. Ma et al. Intelligent algorithms: new avenues for designing nanophotonic devices. Chin. Opt. Lett., 19, 011301(2021). https://doi.org/10.3788/COL202119.011301

[91] L. Deng, Y. Xu, Y. Liu. Hybrid inverse design of photonic structures by combining optimization methods with neural networks. Photonics Nanostruct. Fundam. Appl., 52, 101073(2022). https://doi.org/10.1016/j.photonics.2022.101073

[92] Y. Xu et al. Software-defined nanophotonic devices and systems empowered by machine learning. Prog. Quantum Electron., 89, 100469(2023). https://doi.org/10.1016/j.pquantelec.2023.100469

[93] S. An et al. A deep learning approach for objective-driven all-dielectric metasurface design. ACS Photonics, 6, 3196-3207(2019). https://doi.org/10.1021/acsphotonics.9b00966

[94] Y. Chen et al. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt. Express, 28, 11618-11633(2020). https://doi.org/10.1364/OE.384875

[95] E. S. Harper, M. N. Weber, M. S. Mills. Machine accelerated nano-targeted inhomogeneous structures, 1-5(2019). https://doi.org/10.1109/RAPID.2019.8864295

[96] O. Hemmatyar et al. Full color generation with Fano-type resonant HfO2 nanopillars designed by a deep-learning approach. Nanoscale, 11, 21266-21274(2019). https://doi.org/10.1039/C9NR07408B

[97] J. Jiang, J. A. Fan. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett., 19, 5366-5372(2019). https://doi.org/10.1021/acs.nanolett.9b01857

[98] J. Jiang, J. A. Fan. Simulator-based training of generative neural networks for the inverse design of metasurfaces. Nanophotonics, 9, 1059-1069(2020). https://doi.org/10.1515/nanoph-2019-0330

[99] J. Jiang et al. Free-form diffractive metagrating design based on generative adversarial networks. ACS Nano, 13, 8872-8878(2019). https://doi.org/10.1021/acsnano.9b02371

[100] Z. A. Kudyshev et al. Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization. Appl. Phys. Rev., 7, 021407(2020). https://doi.org/10.1063/1.5134792

[101] Z. Liu et al. A hybrid strategy for the discovery and design of photonic structures. IEEE J. Emerg. Sel. Top. Circuits Syst., 10, 126-135(2020). https://doi.org/10.1109/JETCAS.2020.2970080

[102] Z. Liu et al. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques. Adv. Mater., 32, 1904790(2020). https://doi.org/10.1002/adma.201904790

[103] Z. Liu et al. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018). https://doi.org/10.1021/acs.nanolett.8b03171

[104] R. Lupoiu et al. Ultra-fast optimization of aperiodic metasurface superpixels using conditional physics-augmented deep learning(2023).

[105] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326-6334(2018). https://doi.org/10.1021/acsnano.8b03569

[106] W. Ma et al. Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater., 34, 2110022(2022). https://doi.org/10.1002/adma.202110022

[107] I. Sajedian, H. Lee, J. Rho. Double-deep Q-learning to increase the efficiency of metasurface holograms. Sci. Rep., 9, 10899(2019). https://doi.org/10.1038/s41598-019-47154-z

[108] F. Wen, J. Jiang, J. A. Fan. Robust freeform metasurface design based on progressively growing generative networks. ACS Photonics, 7, 2098-2104(2020). https://doi.org/10.1021/acsphotonics.0c00539

[109] T. Badloe, I. Kim, J. Rho. Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning. Phys. Chem. Chem. Phys., 22, 2337-2342(2020). https://doi.org/10.1039/C9CP05621A

[110] Y. Chen, L. Dal Negro. Physics-informed neural networks for imaging and parameter retrieval of photonic nanostructures from near-field data. APL Photonics, 7, 010802(2022). https://doi.org/10.1063/5.0072969

[111] Y. Kiarashinejad, S. Abdollahramezani, A. Adibi. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures. NPJ Comput. Mater., 6, 12(2020). https://doi.org/10.1038/s41524-020-0276-y

[112] Y. Li et al. Self-learning perfect optical chirality via a deep neural network. Phys. Rev. Lett., 123, 213902(2019). https://doi.org/10.1103/PhysRevLett.123.213902

[113] I. Malkiel et al. Plasmonic nanostructure design and characterization via deep learning. Light: Sci. Appl., 7, 60(2018). https://doi.org/10.1038/s41377-018-0060-7

[114] J. Peurifoy et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv., 4, eaar4206(2018). https://doi.org/10.1126/sciadv.aar4206

[115] R. Riganti, L. D. Negro. Auxiliary physics-informed neural networks for forward, inverse, and coupled radiative transfer problems. Appl. Phys. Lett., 123(2023). https://doi.org/10.1063/5.0167155

[116] S. So, J. Rho. Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics, 8, 1255-1261(2019). https://doi.org/10.1515/nanoph-2019-0117

[117] P. R. Wiecha, O. L. Muskens. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Lett., 20, 329-338(2019). https://doi.org/10.1021/acs.nanolett.9b03971

[118] C. Yeung et al. Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms. ACS Photonics, 7, 2309-2318(2020). https://doi.org/10.1021/acsphotonics.0c01067

[119] M. Zandehshahvar et al. Metric learning: harnessing the power of machine learning in nanophotonics. ACS Photonics, 10, 900-909(2023). https://doi.org/10.1021/acsphotonics.2c01331

[120] M. Zandehshahvar et al. Manifold learning for knowledge discovery and intelligent inverse design of photonic nanostructures: breaking the geometric complexity. ACS Photonics, 9, 714-721(2022). https://doi.org/10.1021/acsphotonics.1c01888

[121] T. Asano, S. Noda. Optimization of photonic crystal nanocavities based on deep learning. Opt. Express, 26, 32704-32717(2018). https://doi.org/10.1364/OE.26.032704

[122] R. Deng, W. Liu, L. Shi. Inverse design in photonic crystals. Nanophotonics, 13, 1219-1237(2024). https://doi.org/10.1515/nanoph-2023-0750

[123] Y. Long et al. Inverse design of photonic topological state via machine learning. Appl. Phys. Lett., 114, 181105(2019). https://doi.org/10.1063/1.5094838

[124] Y. Tang et al. Generative deep learning model for a multi-level nano-optic broadband power splitter(2020).

[125] X. Xu et al. Inverse design of nanophotonic devices using generative adversarial networks with the sim-NN model and self-attention mechanism. Micromachines, 14, 634(2023). https://doi.org/10.3390/mi14030634

[126] C. Yeung et al. Enhancing adjoint optimization-based photonic inverse design with explainable machine learning. ACS Photonics, 9, 1577-1585(2022). https://doi.org/10.1021/acsphotonics.1c01636

[127] I. Sajedian, T. Badloe, J. Rho. Optimisation of colour generation from dielectric nanostructures using reinforcement learning. Opt. Express, 27, 5874-5883(2019). https://doi.org/10.1364/OE.27.005874

[128] D. Gostimirovic, W. N. Ye. An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers. IEEE J. Sel. Top. Quantum Electron., 25, 8200205(2018). https://doi.org/10.1109/JSTQE.2018.2885486

[129] O. Buchnev et al. Deep-learning-assisted focused ion beam nanofabrication. Nano Lett., 22, 2734-2739(2022). https://doi.org/10.1021/acs.nanolett.1c04604

[130] H. Tünnermann, A. Shirakawa. Deep reinforcement learning for coherent beam combining applications. Opt. Express, 27, 24223-24230(2019). https://doi.org/10.1364/OE.27.024223

[131] K. O’Shea. An introduction to convolutional neural networks(2015).

[132] I. Goodfellow et al. Generative adversarial networks. Commun. ACM, 63, 139-144(2020). https://doi.org/10.1145/3422622

[133] M. Mohebbi Moghaddam et al. Games of GANs: game-theoretical models for generative adversarial networks. Artif. Intell. Rev., 56, 9771-9807(2023). https://doi.org/10.1007/s10462-023-10395-6

[134] D. P. Kingma, M. Welling. An introduction to variational autoencoders. Found. Trends® Mach. Learn., 12, 307-392(2019). https://doi.org/10.1561/2200000056

[135] M. Raissi, P. Perdikaris, G. E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378, 686-707(2019). https://doi.org/10.1016/j.jcp.2018.10.045

[136] D. C. Dobson, S. J. Cox. Maximizing band gaps in two-dimensional photonic crystals. SIAM J. Appl. Math., 59, 2108-2120(1999). https://doi.org/10.1137/S0036139998338455

[137] J. S. Jensen, O. Sigmund. Systematic design of photonic crystal structures using topology optimization: low-loss waveguide bends. Appl. Phys. Lett., 84, 2022-2024(2004). https://doi.org/10.1063/1.1688450

[138] P. I. Borel et al. Topology optimization and fabrication of photonic crystal structures. Opt. Express, 12, 1996-2001(2004). https://doi.org/10.1364/OPEX.12.001996

[139] J. Lu, J. Vučković. Objective-first design of high-efficiency, small-footprint couplers between arbitrary nanophotonic waveguide modes. Opt. Express, 20, 7221-7236(2012). https://doi.org/10.1364/OE.20.007221

[140] J. Lu, J. Vučković. Nanophotonic computational design. Opt. Express, 21, 13351-13367(2013). https://doi.org/10.1364/OE.21.013351

[141] X. Huan, Y. M. Marzouk. Gradient-based stochastic optimization methods in Bayesian experimental design. Int. J. Uncertain. Quantif., 4, 479-510(2014). https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014006730

[142] K. Wang et al. Inverse design of digital nanophotonic devices using the adjoint method. Photonics Res., 8, 528-533(2020). https://doi.org/10.1364/PRJ.383887

[143] A. Y. Piggott et al. Fabrication-constrained nanophotonic inverse design. Sci. Rep., 7, 1786(2017). https://doi.org/10.1038/s41598-017-01939-2

[144] A. Y. Piggott et al. Inverse-designed photonics for semiconductor foundries. ACS Photonics, 7, 569-575(2020). https://doi.org/10.1021/acsphotonics.9b01540

[145] C. Shang et al. Inverse-designed lithium niobate nanophotonics. ACS Photonics, 10, 1019-1026(2023). https://doi.org/10.1021/acsphotonics.3c00040

[146] A. O. Dasdemir, V. Minden, E. S. Magden. Computational scaling in inverse photonic design through factorization caching. Appl. Phys. Lett., 123, 221106(2023). https://doi.org/10.1063/5.0172019

[147] Z. Du et al. Ultracompact and multifunctional integrated photonic platform. Sci. Adv., 10, eadm7569(2024). https://doi.org/10.1126/sciadv.adm7569

[148] A. Y. Piggott et al. Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer. Nat. Photonics, 9, 374-377(2015). https://doi.org/10.1038/nphoton.2015.69

[149] L. Su et al. Inverse design and demonstration of a compact on-chip narrowband three-channel wavelength demultiplexer. ACS Photonics, 5, 301-305(2018). https://doi.org/10.1021/acsphotonics.7b00987

[150] M. F. Schubert et al. Inverse design of photonic devices with strict foundry fabrication constraints. ACS Photonics, 9, 2327-2336(2022). https://doi.org/10.1021/acsphotonics.2c00313

[151] K. Y. Yang et al. Inverse-designed non-reciprocal pulse router for chip-based LiDAR. Nat. Photonics, 14, 369-374(2020). https://doi.org/10.1038/s41566-020-0606-0

[152] A. Michaels, E. Yablonovitch. Inverse design of near unity efficiency perfectly vertical grating couplers. Opt. Express, 26, 4766-4779(2018). https://doi.org/10.1364/OE.26.004766

[153] L. Su et al. Fully-automated optimization of grating couplers. Opt. Express, 26, 4023-4034(2018). https://doi.org/10.1364/OE.26.004023

[154] N. V. Sapra et al. Inverse design and demonstration of broadband grating couplers. IEEE J. Sel. Top. Quantum Electron., 25, 6100207(2019). https://doi.org/10.1109/JSTQE.2019.2891402

[155] N. V. Sapra et al. On-chip integrated laser-driven particle accelerator. Science, 367, 79-83(2020). https://doi.org/10.1126/science.aay5734

[156] C. Dory et al. Inverse-designed diamond photonics. Nat. Commun., 10, 3309(2019). https://doi.org/10.1038/s41467-019-11343-1

[157] L. Su et al. Nanophotonic inverse design with SPINS: software architecture and practical considerations. Appl. Phys. Rev., 7, 011407(2020). https://doi.org/10.1063/1.5131263

[158] E. Bayati et al. Inverse designed metalenses with extended depth of focus. ACS Photonics, 7, 873-878(2020). https://doi.org/10.1021/acsphotonics.9b01703

[159] M. Mansouree et al. Large-scale parametrized metasurface design using adjoint optimization. ACS Photonics, 8, 455-463(2021). https://doi.org/10.1021/acsphotonics.0c01058

[160] H. Chung, O. D. Miller. High-NA achromatic metalenses by inverse design. Opt. Express, 28, 6945-6965(2020). https://doi.org/10.1364/OE.385440

[161] A. F. Oskooi et al. MEEP: a flexible free-software package for electromagnetic simulations by the FDTD method. Comput. Phys. Commun., 181, 687-702(2010). https://doi.org/10.1016/j.cpc.2009.11.008

[162] J. Lehtinen. A framework for precomputed and captured light transport. ACM Trans. Graphics, 26, 13(2007). https://doi.org/10.1145/1289603.1289604

[163] J.-K. Byun et al. Application of the sensitivity analysis to the optimal design of the microstrip low-pass filter with defected ground structure. IEEE Trans. Magn., 45, 1462-1465(2009). https://doi.org/10.1109/TMAG.2009.2012680

[164] J. C. Finlay, T. H. Foster. Recovery of hemoglobin oxygen saturation and intrinsic fluorescence with a forward-adjoint model. Appl. Opt., 44, 1917-1933(2005). https://doi.org/10.1364/AO.44.001917

[165] C. M. Lalau-Keraly et al. Adjoint shape optimization applied to electromagnetic design. Opt. Express, 21, 21693-21701(2013). https://doi.org/10.1364/OE.21.021693

[166] A. C. Niederberger et al. Sensitivity analysis and optimization of sub-wavelength optical gratings using adjoints. Opt. Express, 22, 12971-12981(2014). https://doi.org/10.1364/OE.22.012971

[167] G. B. Hoffman et al. Improved broadband performance of an adjoint shape optimized waveguide crossing using a Levenberg-Marquardt update. Opt. Express, 27, 24765-24780(2019). https://doi.org/10.1364/OE.27.024765

[168] M. P. Bendsøe, N. Kikuchi. Generating optimal topologies in structural design using a homogenization method. Comput. Methods Appl. Mech. Eng., 71, 197-224(1988). https://doi.org/10.1016/0045-7825(88)90086-2

[169] X. Guo, W. Zhang, W. Zhong. Doing topology optimization explicitly and geometrically—a new moving morphable components based framework. J. Appl. Mech., 81, 081009(2014). https://doi.org/10.1115/1.4027609

[170] P. I. Borel et al. Topology optimised broadband photonic crystal Y-splitter. Electron. Lett., 41, 69-71(2005). https://doi.org/10.1049/el:20057717

[171] J. S. Jensen et al. Topology design and fabrication of an efficient double 90/spl deg/photonic crystal waveguide bend. IEEE Photonics Technol. Lett., 17, 1202-1204(2005). https://doi.org/10.1109/LPT.2005.846502

[172] N. Ikeda et al. Topology optimised photonic crystal waveguide intersections with high-transmittance and low crosstalk. Electron. Lett., 42, 1031(2006). https://doi.org/10.1049/el:20062027

[173] J. Riishede, O. Sigmund. Inverse design of dispersion compensating optical fiber using topology optimization. J. Opt. Soc. Am. B, 25, 88-97(2007). https://doi.org/10.1364/JOSAB.25.000088

[174] L. Yang et al. Design of one-dimensional optical pulse-shaping filters by time-domain topology optimization. Appl. Phys. Lett., 95, 261101(2009). https://doi.org/10.1063/1.3278595

[175] J. S. Jensen, O. Sigmund. Topology optimization for nano-photonics. Laser Photonics Rev., 5, 308-321(2011). https://doi.org/10.1002/lpor.201000014

[176] R. E. Christiansen, O. Sigmund. Compact 200 line MATLAB code for inverse design in photonics by topology optimization: tutorial. J. Opt. Soc. Am. B, 38, 510-520(2021). https://doi.org/10.1364/JOSAB.405955

[177] M. P. Bendsøe. Optimal shape design as a material distribution problem. Struct. Optim., 1, 193-202(1989). https://doi.org/10.1007/BF01650949

[178] S. Osher, R. Fedkiw, K. Piechor. Level set methods and dynamic implicit surfaces. Appl. Mech. Rev., 57, B15(2004). https://doi.org/10.1115/1.1760520

[179] X. Huang, Y. M. Xie, M. C. Burry. Manufacturing. A new algorithm for bi-directional evolutionary structural optimization. JSME Int. J. Ser. C Mech. Syst. Mach. Elements, 49, 1091-1099(2006). https://doi.org/10.1299/jsmec.49.1091

[180] Z. Lin et al. Topology optimization of freeform large-area metasurfaces. Opt. Express, 27, 15765-15775(2019). https://doi.org/10.1364/OE.27.015765

[181] Z. Lin et al. Topology-optimized multilayered metaoptics. Phys. Rev. Appl., 9, 044030(2018). https://doi.org/10.1103/PhysRevApplied.9.044030

[182] J. A. Fan. Freeform metasurface design based on topology optimization. MRS Bull., 45, 196-201(2020). https://doi.org/10.1557/mrs.2020.62

[183] R. E. Christiansen et al. Fullwave Maxwell inverse design of axisymmetric, tunable, and multi-scale multi-wavelength metalenses. Opt. Express, 28, 33854-33868(2020). https://doi.org/10.1364/OE.403192

[184] Z. Lin et al. Computational inverse design for ultra-compact single-piece metalenses free of chromatic and angular aberration. Appl. Phys. Lett., 118, 041104(2021). https://doi.org/10.1063/5.0035419

[185] R. E. Christiansen et al. Inverse design of nanoparticles for enhanced Raman scattering. Opt. Express, 28, 4444-4462(2020). https://doi.org/10.1364/OE.28.004444

[186] R. E. Christiansen, F. Wang, O. Sigmund. Topological insulators by topology optimization. Phys. Rev. Lett., 122, 234502(2019). https://doi.org/10.1103/PhysRevLett.122.234502

[187] Y. Li et al. Unsupervised learning of non-Hermitian photonic bulk topology. Laser Photonics Rev., 17, 2300481(2023). https://doi.org/10.1002/lpor.202300481

[188] Y. Augenstein, C. Rockstuhl. Inverse design of nanophotonic devices with structural integrity. ACS Photonics, 7, 2190-2196(2020). https://doi.org/10.1021/acsphotonics.0c00699

[189] L. He et al. Super-compact universal quantum logic gates with inverse-designed elements. Sci. Adv., 9, eadg6685(2023). https://doi.org/10.1126/sciadv.adg6685

[190] J. Gedeon, E. Hassan, A. Calà Lesina. Time-domain topology optimization of arbitrary dispersive materials for broadband 3D nanophotonics inverse design. ACS Photonics, 10, 3875-3887(2023). https://doi.org/10.1021/acsphotonics.3c00572

[191] T. Weise. Global Optimization Algorithms-Theory and Application, 361, 153(2009).

[192] A. V. Pogrebnyakov et al. Reconfigurable near-IR metasurface based on Ge₂Sb₂Te₅ phase-change material. Opt. Mater. Express, 8, 2264-2275(2018). https://doi.org/10.1364/OME.8.002264

[193] Z. Li et al. Broadband infrared binary-pattern metasurface absorbers with micro-genetic algorithm optimization. Opt. Lett., 44, 114-117(2018). https://doi.org/10.1364/OL.44.000114

[194] Z. Li et al. Strong circular dichroism in chiral plasmonic metasurfaces optimized by micro-genetic algorithm. Opt. Express, 27, 28313-28323(2019). https://doi.org/10.1364/OE.27.028313

[195] C. Liu, S. A. Maier, G. Li. Genetic-algorithm-aided meta-atom multiplication for improved absorption and coloration in nanophotonics. ACS Photonics, 7, 1716-1722(2020). https://doi.org/10.1021/acsphotonics.0c00266

[196] Y. Fan et al. Phase-controlled metasurface design via optimized genetic algorithm. Nanophotonics, 9, 3931-3939(2020). https://doi.org/10.1515/nanoph-2020-0132

[197] C. Lu et al. Nanophotonic polarization routers based on an intelligent algorithm. Adv. Opt. Mater., 8, 1902018(2020). https://doi.org/10.1002/adom.201902018

[198] E. Lucas et al. Tailoring microcombs with inverse-designed, meta-dispersion microresonators. Nat. Photonics, 17, 943-950(2023). https://doi.org/10.1038/s41566-023-01252-7

[199] J. C. Mak et al. Binary particle swarm optimized 2 × 2 power splitters in a standard foundry silicon photonic platform. Opt. Lett., 41, 3868-3871(2016). https://doi.org/10.1364/OL.41.003868

[200] L. Leng et al. Ultra-broadband, fabrication tolerant optical coupler for arbitrary splitting ratio using particle swarm optimization algorithm. IEEE Photonics J., 12, 6602212(2020). https://doi.org/10.1109/JPHOT.2020.3029059

[201] R. Yan et al. Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning. Nanotechnology, 31, 375202(2020). https://doi.org/10.1088/1361-6528/ab95b8

[202] Y. Ha et al. Minimized two-and four-step varifocal lens based on silicon photonic integrated nanoapertures. Opt. Express, 28, 7943-7952(2020). https://doi.org/10.1364/OE.386418

[203] L. Han et al. Improved particle swarm optimization algorithm for high performance SPR sensor design. Appl. Opt., 60, 1753-1760(2021). https://doi.org/10.1364/AO.417015

[204] C. Babayigit et al. Inverse designed photonic crystals for spatial filtering. Appl. Phys. Lett., 122, 244103(2023). https://doi.org/10.1063/5.0150756

[205] X. Guo et al. Design of broadband omnidirectional antireflection coatings using ant colony algorithm. Opt. Express, 22, A1137-A1144(2014). https://doi.org/10.1364/OE.22.0A1137

[206] D. Z. Zhu, P. L. Werner, D. H. Werner. Design and optimization of 3-D frequency-selective surfaces based on a multiobjective lazy ant colony optimization algorithm. IEEE Trans. Antennas Propag., 65, 7137-7149(2017). https://doi.org/10.1109/TAP.2017.2766660

[207] D. Z. Zhu et al. Fabrication and characterization of multiband polarization independent 3-D-printed frequency selective structures with ultrawide fields of view. IEEE Trans. Antennas Propag., 66, 6096-6105(2018). https://doi.org/10.1109/TAP.2018.2866507

[208] G. N. Malheiros-Silveira, F. G. Delalibera. Inverse design of photonic structures using an artificial bee colony algorithm. Appl. Opt., 59, 4171-4175(2020). https://doi.org/10.1364/AO.389475

[209] E. Garoudja et al. Artificial bee colony algorithm: a novel strategy for optical constants and thin film thickness extraction using only optical transmittance spectra for photovoltaic applications. Optik, 241, 167030(2021). https://doi.org/10.1016/j.ijleo.2021.167030

[210] M. Ali, H. Alasadi, N. Ali Noori. Optimization noise figure of fiber Raman amplifier based on bat algorithm in optical communication network. Int. J. Eng. Technol., 7, 874(2018). https://doi.org/10.14419/ijet.v7i2.11062

[211] C. Y. Liao et al. An improved bat algorithm for more efficient and faster maximum power point tracking for a photovoltaic system under partial shading conditions. IEEE Access, 8, 96378-96390(2020). https://doi.org/10.1109/ACCESS.2020.2993361

[212] Q. Zhao et al. Parameter-free optimization algorithm for iterative wavefront shaping. Opt. Lett., 46, 2880-2883(2021). https://doi.org/10.1364/OL.427215

[213] G. Sun et al. Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm. Comput. Networks, 116, 63-78(2017). https://doi.org/10.1016/j.comnet.2017.02.014

[214] D. Li et al. Variable step size adaptive cuckoo search optimization algorithm for phase diversity. Appl. Opt., 57, 8212-8219(2018). https://doi.org/10.1364/AO.57.008212

[215] E. Bor, M. Turduev, H. Kurt. Differential evolution algorithm based photonic structure design: numerical and experimental verification of subwavelength λ/5 focusing of light. Sci. Rep., 6, 30871(2016). https://doi.org/10.1038/srep30871

[216] Y. Xie et al. Design of an arbitrary ratio optical power splitter based on a discrete differential multiobjective evolutionary algorithm. Appl. Opt., 59, 1780-1785(2020). https://doi.org/10.1364/AO.382215

[217] L. Lei et al. A characterization method of thin film parameters based on adaptive differential evolution algorithm. IEEE Access, 9, 90231-90243(2021). https://doi.org/10.1109/ACCESS.2021.3090468

[218] P. E. Sieber, D. H. Werner. Infrared broadband quarter-wave and half-wave plates synthesized from anisotropic Bézier metasurfaces. Opt. Express, 22, 32371-32383(2014). https://doi.org/10.1364/OE.22.032371

[219] G. Fujii, M. Takahashi, Y. Akimoto. CMA-ES-based structural topology optimization using a level set boundary expression—application to optical and carpet cloaks. Comput. Methods Appl. Mech. Eng., 332, 624-643(2018). https://doi.org/10.1016/j.cma.2018.01.008

[220] M. A. Barry et al. Evolutionary algorithms converge towards evolved biological photonic structures. Sci. Rep., 10, 12024(2020). https://doi.org/10.1038/s41598-020-68719-3

[221] Z. Gao, Z. Zhang, D. S. Boning. Automatic synthesis of broadband silicon photonic devices via Bayesian optimization. J. Lightwave Technol., 40, 7879-7892(2022). https://doi.org/10.1109/JLT.2022.3207052

[222] M. Li et al. Bayesian optimization of nanophotonic electromagnetic shielding with very high visible transparency. Opt. Express, 30, 33182-33194(2022). https://doi.org/10.1364/OE.468843

[223] F. Qin et al. Designing metal-dielectric nanoantenna for unidirectional scattering via Bayesian optimization. Opt. Express, 27, 31075-31086(2019). https://doi.org/10.1364/OE.27.031075

[224] D. Zhang et al. Segmented Bayesian optimization of meta-gratings for sub-wavelength light focusing. J. Opt. Soc. Am. B, 37, 181-187(2019). https://doi.org/10.1364/JOSAB.37.000181

[225] S. Forrest. Genetic algorithms: principles of natural selection applied to computation. Science, 261, 872-878(1993). https://doi.org/10.1126/science.8346439

[226] J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence(1992).

[227] J. H. Holland. Genetic algorithms. Sci. Am., 267, 66-72(1992). https://doi.org/10.1038/scientificamerican0792-66

[228] J. Kennedy, R. Eberhart. Particle swarm optimization, 4, 1942-1948(1995).

[229] I. C. Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett., 85, 317-325(2003). https://doi.org/10.1016/S0020-0190(02)00447-7

[230] K. Hara, T. Iwamoto, K. Kyuma. Optimum design technique for optoelectronic devices using simulated annealing. Electron. Commun. Jpn., 79, 22-32(1996). https://doi.org/10.1002/ecjb.4420790103

[231] X. Cheng et al. Design of X-ray super-mirrors using simulated annealing algorithm. Opt. Commun., 265, 197-206(2006). https://doi.org/10.1016/j.optcom.2006.03.027

[232] Y.-J. Chang, Y.-T. Chen. Broadband omnidirectional antireflection coatings for metal-backed solar cells optimized using simulated annealing algorithm incorporated with solar spectrum. Opt. Express, 19, A875-A887(2011). https://doi.org/10.1364/OE.19.00A875

[233] Y. Zhao et al. Broadband diffusion metasurface based on a single anisotropic element and optimized by the simulated annealing algorithm. Sci. Rep., 6, 23896(2016). https://doi.org/10.1038/srep23896

[234] Z. Xie et al. Broadband on-chip photonic spin Hall element via inverse design. Photonics Res., 8, 121-126(2020). https://doi.org/10.1364/PRJ.8.000121

[235] S. Kirkpatrick, C. D. Gelattm, M. P. Vecchi. Optimization by simulated annealing. Science, 220, 671-680(1983). https://doi.org/10.1126/science.220.4598.671

[236] M. Čepin. Assessment of Power System Reliability: Methods and Applications(2011).

[237] A. Basu, L. N. Frazer. Rapid determination of the critical temperature in simulated annealing inversion. Science, 249, 1409-1412(1990). https://doi.org/10.1126/science.249.4975.1409

[238] A. Khajeh et al. Tunable broadband polarization converters based on coded graphene metasurfaces. Sci. Rep., 11, 1296(2021). https://doi.org/10.1038/s41598-020-80493-w

[239] T. Lin et al. Design of mechanically-tunable photonic crystal split-beam nanocavity. Opt. Lett., 40, 3504-3507(2015). https://doi.org/10.1364/OL.40.003504

[240] I. Galaktionov et al. The use of modified hill-climbing algorithm for laser beam focusing through the turbid medium. Proc. SPIE, 10090, 100901K(2017). https://doi.org/10.1117/12.2257447

[241] St. J. Russell, P. Norvig. Artificial Intelligence: A Modern Approach(2016).

[242] E. Talbi. Metaheuristics: From Design to Implementation, 2, 268-308(2009).

[243] D. Gagnon et al. Optimization of integrated polarization filters. Opt. Lett., 39, 5768-5771(2014). https://doi.org/10.1364/OL.39.005768

[244] D. Gagnon, J. Dumont, L. J. Dubé. Multiobjective optimization in integrated photonics design. Opt. Lett., 38, 2181-2184(2013). https://doi.org/10.1364/OL.38.002181

[245] M. A. Seldowitz, J. P. Allebach, D. W. Sweeney. Synthesis of digital holograms by direct binary search. Appl. Opt., 26, 2788-2798(1987). https://doi.org/10.1364/AO.26.002788

[246] B. B. Chhetri, S. Yang, T. Shimomura. Stochastic approach in the efficient design of the direct-binary-search algorithm for hologram synthesis. Appl. Opt., 39, 5956-5964(2000). https://doi.org/10.1364/AO.39.005956

[247] B. Shen et al. An integrated-nanophotonics polarization beamsplitter with 2.4×2.4 μm2 footprint. Nat. Photonics, 9, 378-382(2015). https://doi.org/10.1038/nphoton.2015.80

[248] B. Shen, P. Wang, R. Menon. Optimization and analysis of 3D nanostructures for power-density enhancement in ultra-thin photovoltaics under oblique illumination. Opt. Express, 22, A311-A319(2014). https://doi.org/10.1364/OE.22.00A311

[249] B. Shen et al. Ultra-high-efficiency metamaterial polarizer. Optica, 1, 356-360(2014). https://doi.org/10.1364/OPTICA.1.000356

[250] P. Wang, R. Menon. Optimization of periodic nanostructures for enhanced light-trapping in ultra-thin photovoltaics. Opt. Express, 21, 6274-6285(2013). https://doi.org/10.1364/OE.21.006274

[251] G. Kim et al. Design and analysis of multi-wavelength diffractive optics. Opt. Express, 20, 2814-2823(2012). https://doi.org/10.1364/OE.20.002814

[252] H. Chen et al. A gradient-oriented binary search method for photonic device design. J. Lightwave Technol., 39, 2407-2412(2021). https://doi.org/10.1109/JLT.2021.3050771

[253] H. Ma et al. Arbitrary-direction, multichannel and ultra-compact power splitters by inverse design method. Opt. Commun., 462, 125329(2020). https://doi.org/10.1016/j.optcom.2020.125329

[254] H. Xie et al. An ultra-compact 3-dB power splitter for three modes based on pixelated meta-structure. IEEE Photonics Technol. Lett., 32, 341-344(2020). https://doi.org/10.1109/LPT.2020.2975128

[255] W. Chang et al. Inverse design and demonstration of an ultracompact broadband dual-mode 3 dB power splitter. Opt. Express, 26, 24135-24144(2018). https://doi.org/10.1364/OE.26.024135

[256] Y. Liu et al. Subwavelength polarization splitter–rotator with ultra-compact footprint. Opt. Lett., 44, 4495-4498(2019). https://doi.org/10.1364/OL.44.004495

[257] M. P. Edgar, G. M. Gibson, M. J. Padgett. Principles and prospects for single-pixel imaging. Nat. Photonics, 13, 13-20(2019). https://doi.org/10.1038/s41566-018-0300-7

[258] V. Boominathan et al. Recent advances in lensless imaging. Optica, 9, 1-16(2022). https://doi.org/10.1364/OPTICA.431361

[259] K. G. Nalbant, Ş. Uyanık. Computer vision in the metaverse. J. Metaverse, 3, 9-18(2021). https://doi.org/10.57019/jmv.1148015

[260] M. Xiang et al. Computational optical imaging: challenges, opportunities, new trends, and emerging applications. Front. Imaging, 3, 1336829(2024). https://doi.org/10.3389/fimag.2024.1336829

[261] A. Pan et al. Computational imaging: the next revolution for biophotonics and biomedicine. Cells, 13, 433(2024). https://doi.org/10.3390/cells13050433

[262] B. Bhanu. Automatic target recognition: state of the art survey. IEEE Trans. Aerosp. Electron. Syst., AES-22, 364-379(1986). https://doi.org/10.1109/TAES.1986.310772

[263] B. P. Abbott et al. GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Lett., 119, 161101(2017). https://doi.org/10.1103/PhysRevLett.119.161101

[264] M. T. McCann, K. H. Jin, M. Unser. Convolutional neural networks for inverse problems in imaging: a review. IEEE Signal Process Mag., 34, 85-95(2017). https://doi.org/10.1109/MSP.2017.2739299

[265] S. Yoon et al. Deep optical imaging within complex scattering media. Nat. Rev. Phys., 2, 141-158(2020). https://doi.org/10.1038/s42254-019-0143-2

[266] J. A. Fessler. Optimization methods for magnetic resonance image reconstruction: key models and optimization algorithms. IEEE Signal Process Mag., 37, 33-40(2020). https://doi.org/10.1109/MSP.2019.2943645

[267] A. S. Panayides et al. AI in medical imaging informatics: current challenges and future directions. IEEE J. Biomed. Health. Inf., 24, 1837-1857(2020). https://doi.org/10.1109/JBHI.2020.2991043

[268] W. Liang et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell., 4, 669-677(2022). https://doi.org/10.1038/s42256-022-00516-1

[269] X.-F. Han et al. A review of algorithms for filtering the 3D point cloud. Signal Process. Image Commun., 57, 103-112(2017). https://doi.org/10.1016/j.image.2017.05.009

[270] A. Voulodimos et al. Deep learning for computer vision: a brief review. Comput. Intell. Neurosci., 2018, 7068349(2018). https://doi.org/10.1155/2018/7068349

[271] V. Wiley, T. Lucas. Computer vision and image processing: a paper review. Int. J. Artif. Intell. Res., 2, 22-36(2018). https://doi.org/10.29099/ijair.v2i1.42

[272] A. Agrawal. Application of machine learning to computer graphics. IEEE Comput. Graphics Appl., 38, 93-96(2018). https://doi.org/10.1109/MCG.2018.042731662

[273] T. L. Nguyen et al. Quantitative phase imaging: recent advances and expanding potential in biomedicine. ACS Nano, 16, 11516-11544(2022). https://doi.org/10.1021/acsnano.1c11507

[274] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018). https://doi.org/10.1038/s41566-018-0253-x

[275] Y. Li et al. Imaging through diffusers with extended depth-of-field using a deep neural network(2020).

[276] L. Tian. Deep learning augmented microscopy: a faster, wider view, higher resolution autofluorescence-harmonic microscopy. Light Sci. Appl., 11, 109(2022). https://doi.org/10.1038/s41377-022-00801-z

[277] Q. Tian et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system. Opt. Express, 27, 10765-10776(2019). https://doi.org/10.1364/OE.27.010765

[278] I. Vishniakou, J. D. Seelig. Wavefront correction for adaptive optics with reflected light and deep neural networks. Opt. Express, 28, 15459-15471(2020). https://doi.org/10.1364/OE.392794

[279] J. Wang et al. Quantitative phase imaging with a compact meta-microscope. NPJ Nanophotonics, 1, 4(2024). https://doi.org/10.1038/s44310-024-00007-8

[280] Y. Xue et al. Reliable deep-learning-based phase imaging with uncertainty quantification. Optica, 6, 618-629(2019). https://doi.org/10.1364/OPTICA.6.000618

[281] K. Wang et al. One-step robust deep learning phase unwrapping. Opt. Express, 27, 15100-15115(2019). https://doi.org/10.1364/OE.27.015100

[282] Z. Wu et al. Generalized phase unwrapping method that avoids jump errors for fringe projection profilometry. Opt. Express, 29, 27181-27192(2021). https://doi.org/10.1364/OE.436116

[283] H. An et al. Temporal phase unwrapping based on unequal phase-shifting code. IEEE Trans. Image Process., 32, 1432-1441(2023). https://doi.org/10.1109/TIP.2023.3244650

[284] G. Spoorthi, R. K. S. S. Gorthi, S. Gorthi. PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach. IEEE Trans. Image Process., 29, 4862-4872(2020). https://doi.org/10.1109/TIP.2020.2977213

[285] H. Wang et al. A novel quality-guided two-dimensional InSAR phase unwrapping method via GAUNet. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 7840-7856(2021). https://doi.org/10.1109/JSTARS.2021.3099485

[286] Z. Zhao et al. Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network. Opt. Lasers Eng., 138, 106405(2021). https://doi.org/10.1016/j.optlaseng.2020.106405

[287] J. C. Zhang et al. Phase unwrapping in optical metrology via denoised and convolutional segmentation networks. Opt. Express, 27, 14903-14912(2019). https://doi.org/10.1364/OE.27.014903

[288] S. Park, Y. Kim, I. Moon. Automated phase unwrapping in digital holography with deep learning. Biomed. Opt. Express, 12, 7064-7081(2021). https://doi.org/10.1364/BOE.440338

[289] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl., 7, 17141(2018). https://doi.org/10.1038/lsa.2017.141

[290] T. Nguyen et al. Deep learning approach for Fourier ptychography microscopy. Opt. Express, 26, 26470-26484(2018). https://doi.org/10.1364/OE.26.026470

[291] L. F. Zheng et al. Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network. Biomed. Opt. Express, 10, 2446-2456(2019). https://doi.org/10.1364/BOE.10.002446

[292] T. Zhang et al. Rapid and robust two-dimensional phase unwrapping via deep learning. Opt. Express, 27, 23173-23185(2019). https://doi.org/10.1364/OE.27.023173

[293] W. Lu et al. High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network. Opt. Express, 30, 37457-37469(2022). https://doi.org/10.1364/OE.470534

[294] G.-Z. Yang. Gerchberg–Saxton and Yang–Gu algorithms for phase retrieval in a nonunitary transform system: a comparison. Appl. Opt., 33, 209-218(1994). https://doi.org/10.1364/AO.33.000209

[295] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758-2769(1982). https://doi.org/10.1364/AO.21.002758

[296] J. R. Fienup. Phase-retrieval algorithms for a complicated optical system. Appl. Opt., 32, 1737-1746(1993). https://doi.org/10.1364/AO.32.001737

[297] T. Brown, M. M. Wang. An iterative algorithm for single-frequency estimation. IEEE Trans. Signal Process., 50, 2671-2682(2002). https://doi.org/10.1109/TSP.2002.804096

[298] A. Kreimer, D. Raphaeli. Efficient low-complexity phase noise resistant iterative joint phase estimation and decoding algorithm. IEEE Trans. Commun., 66, 4199-4210(2018). https://doi.org/10.1109/TCOMM.2018.2829865

[299] J. Li. Iterative method to improve the precision of the quantum-phase-estimation algorithm. Phys. Rev. A, 109, 032606(2024). https://doi.org/10.1103/PhysRevA.109.032606

[300] Y. Chen, Q. Kemao. Advanced iterative algorithm for phase extraction: performance evaluation and enhancement. Opt. Express, 27, 37634-37651(2019). https://doi.org/10.1364/OE.27.037634

[301] Y. Y. D. Wang et al. High-generalization deep sparse pattern reconstruction: feature extraction of speckles using self-attention armed convolutional neural networks. Opt. Express, 29, 35702-35711(2021). https://doi.org/10.1364/OE.440405

[302] T. Shimobaba et al. Dynamic-range compression scheme for digital hologram using a deep neural network. Opt. Lett., 44, 3038-3041(2019). https://doi.org/10.1364/OL.44.003038

[303] Y. Y. Ma, X. H. Feng, L. Gao. Deep-learning-based image reconstruction for compressed ultrafast photography. Opt. Lett., 45, 4400-4403(2020). https://doi.org/10.1364/OL.397717

[304] X. B. Li et al. Learning-based denoising for polarimetric images. Opt. Express, 28, 16309-16321(2020). https://doi.org/10.1364/OE.391017

[305] J. Meng et al. Learning based polarization image fusion under an alternative paradigm. Opt. Laser Technol., 168, 109969(2024). https://doi.org/10.1016/j.optlastec.2023.109969

[306] Y. Lyu et al. Reflection separation using a pair of unpolarized and polarized images(2019).

[307] C. Zhou et al. Learning to dehaze with polarization(2021).

[308] M. Shao et al. Transparent shape from a single view polarization image, 9243-9252(2023).

[309] H. Hu et al. Polarimetric image denoising on small datasets using deep transfer learning. Opt. Laser Technol., 166, 109632(2023). https://doi.org/10.1016/j.optlastec.2023.109632

[310] Z. Li et al. Polarized color image denoising, 9873-9882(2023).

[311] A. Dosovitskiy et al. An image is worth 16 × 16 words: transformers for image recognition at scale(2020).

[312] Y. Y. Sun, J. C. Zhang, R. G. Liang. Color polarization demosaicking by a convolutional neural network. Opt. Lett., 46, 4338-4341(2021). https://doi.org/10.1364/OL.431919

[313] X. B. Liu, X. B. Li, S. C. Chen. Enhanced polarization demosaicking network via a precise angle of polarization loss calculation method. Opt. Lett., 47, 1065-1068(2022). https://doi.org/10.1364/OL.451335

[314] J. Ting et al. Deep snapshot HDR reconstruction based on the polarization camera, 1769-1773(2021).

[315] R. Li et al. Reflection separation via multi-bounce polarization state tracing. Lect. Notes Comput. Sci., 12358, 781-796(2020). https://doi.org/10.1007/978-3-030-58601-0_46

[316] C. Lei et al. Polarized reflection removal with perfect alignment in the wild, 1747-1755(2020).

[317] Z. Zhu et al. PODB: a learning-based polarimetric object detection benchmark for road scenes in adverse weather conditions. Inf. Fusion, 108, 102385(2024). https://doi.org/10.1016/j.inffus.2024.102385

[318] Y. Dong et al. A polarization-imaging-based machine learning framework for quantitative pathological diagnosis of cervical precancerous lesions. IEEE Trans. Med. Imaging, 40, 3728-3738(2021). https://doi.org/10.1109/TMI.2021.3097200

[319] H. Mei et al. Don’t hit me! Glass detection in real-world scenes, 3684-3693(2020). https://doi.org/10.1109/CVPR42600.2020.00374

[320] H. He et al. Enhanced boundary learning for glass-like object segmentation, 15839-15848(2021). https://doi.org/10.1109/ICCV48922.2021.01556

[321] Y. Qiao et al. Multi-view spectral polarization propagation for video glass segmentation, 23161-23171(2023).

[322] H. Mei et al. Glass segmentation using intensity and spectral polarization cues, 12612-12621(2022). https://doi.org/10.1109/CVPR52688.2022.01229

[323] A. Kalra et al. Deep polarization cues for transparent object segmentation, 8599-8608(2020). https://doi.org/10.1109/CVPR42600.2020.00863

[324] Y. Liang et al. Multimodal material segmentation, 19768-19776(2022).

[325] P. Qi et al. U2R-pGAN: unpaired underwater-image recovery with polarimetric generative adversarial network. Opt. Lasers Eng., 157, 107112(2022). https://doi.org/10.1016/j.optlaseng.2022.107112

[326] Y. Ba et al. Deep shape from polarization. Lect. Notes Comput. Sci., 12369, 554-571(2020). https://doi.org/10.1007/978-3-030-58586-0_33

[327] S. Zou et al. 3D human shape reconstruction from a polarization image. Lect. Notes Comput. Sci., 12359, 351-368(2020). https://doi.org/10.1007/978-3-030-58568-6_21

[328] C. Lei et al. Shape from polarization for complex scenes in the wild, 12622-12631(2022). https://doi.org/10.1109/CVPR52688.2022.01230

[329] D. S. Jeon et al. Compact snapshot hyperspectral imaging with diffracted rotation. ACM Trans. Graphic, 38, 117(2019). https://doi.org/10.1145/3306346.3322946

[330] H. Hu et al. Practical snapshot hyperspectral imaging with DOE. Opt. Lasers Eng., 156, 107098(2022). https://doi.org/10.1016/j.optlaseng.2022.107098

[331] W. Zhang et al. Deeply learned broadband encoding stochastic hyperspectral imaging. Light Sci. Appl., 10, 108(2021). https://doi.org/10.1038/s41377-021-00545-2

[332] Q. Cui et al. Snapshot hyperspectral light field tomography. Optica, 8, 1552-1558(2021). https://doi.org/10.1364/OPTICA.440074

[333] H. Arguello et al. Shift-variant color-coded diffractive spectral imaging system. Optica, 8, 1424-1434(2021). https://doi.org/10.1364/OPTICA.439142

[334] K. Monakhova et al. Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array. Optica, 7, 1298-1307(2020). https://doi.org/10.1364/OPTICA.397214

[335] M. Yako et al. Video-rate hyperspectral camera based on a CMOS-compatible random array of Fabry–Pérot filters. Nat. Photonics, 17, 218-223(2023). https://doi.org/10.1038/s41566-022-01141-5

[336] J. Yang et al. Ultraspectral imaging based on metasurfaces with freeform shaped meta-atoms. Laser Photonics Rev., 16, 2100663(2022). https://doi.org/10.1002/lpor.202100663

[337] W. Zhang et al. Handheld snapshot multi-spectral camera at 65-megapixel resolution. Nat. Commun., 14, 5043(2023). https://doi.org/10.1038/s41467-023-40739-3

[338] X. Cao et al. Computational snapshot multispectral cameras: toward dynamic capture of the spectral world. IEEE Signal Process Mag., 33, 95-108(2016). https://doi.org/10.1109/MSP.2016.2582378

[339] Z. Meng, X. Yuan. Perception inspired deep neural networks for spectral snapshot compressive imaging, 2813-2817(2021). https://doi.org/10.1109/ICIP42928.2021.9506316

[340] F. Bao et al. Heat-assisted detection and ranging. Nature, 619, 743-748(2023). https://doi.org/10.1038/s41586-023-06174-6

[341] J. Yoon. Hyperspectral imaging for clinical applications. Biochip J., 16, 1-12(2022). https://doi.org/10.1007/s13206-021-00041-0

[342] X. Li et al. Challenges and opportunities in bioimage analysis. Nat. Methods, 20, 958-961(2023). https://doi.org/10.1038/s41592-023-01900-4

[343] C. McNeil et al. An end-to-end platform for digital pathology using hyperspectral autofluorescence microscopy and deep learning-based virtual histology. Mod. Pathol., 37, 100377(2024). https://doi.org/10.1016/j.modpat.2023.100377

[344] S. Berry et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science, 372, eaba2609(2021). https://doi.org/10.1126/science.aba2609

[345] A. G. Brolo. Plasmonics for future biosensors. Nat. Photonics, 6, 709-713(2012). https://doi.org/10.1038/nphoton.2012.266

[346] H. Kaushal, G. Kaddoum. Applications of lasers for tactical military operations. IEEE Access, 5, 20736-20753(2017). https://doi.org/10.1109/ACCESS.2017.2755678

[347] G. Van Houdt, C. Mosquera, G. Nápoles. A review on the long short-term memory model. Artif. Intell. Rev., 53, 5929-5955(2020). https://doi.org/10.1007/s10462-020-09838-1

[348] E. Ip, J. M. Kahn. Compensation of dispersion and nonlinear impairments using digital backpropagation. J. Lightwave Technol., 26, 3416-3425(2008). https://doi.org/10.1109/JLT.2008.927791

[349] I. D. Phillips et al. Exceeding the nonlinear-Shannon limit using Raman laser based amplification and optical phase conjugation(2014).

[350] S. Zhang et al. Field and lab experimental demonstration of nonlinear impairment compensation using neural networks. Nat. Commun., 10, 3033(2019). https://doi.org/10.1038/s41467-019-10911-9

[351] Q. Zhou, F. Zhang, C. Yang. AdaNN: adaptive neural network-based equalizer via online semi-supervised learning. J. Lightwave Technol., 38, 4315-4324(2020). https://doi.org/10.1109/JLT.2020.2991028

[352] S. Deligiannidis et al. Compensation of fiber nonlinearities in digital coherent systems leveraging long short-term memory neural networks. J. Lightwave Technol., 38, 5991-5999(2020). https://doi.org/10.1109/JLT.2020.3007919

[353] X. Dai et al. LSTM networks enabled nonlinear equalization in 50-Gb/s PAM-4 transmission links. Appl. Opt., 58, 6079-6084(2019). https://doi.org/10.1364/AO.58.006079

[354] B. Karanov et al. Deep learning for communication over dispersive nonlinear channels: performance and comparison with classical digital signal processing, 192-199(2019).

[355] S. Deligiannidis, C. Mesaritakis, A. Bogris. Performance and complexity analysis of bi-directional recurrent neural network models versus Volterra nonlinear equalizers in digital coherent systems. J. Lightwave Technol., 39, 5791-5798(2021). https://doi.org/10.1109/JLT.2021.3092415

[356] P. J. Freire et al. Performance versus complexity study of neural network equalizers in coherent optical systems. J. Lightwave Technol., 39, 6085-6096(2021). https://doi.org/10.1109/JLT.2021.3096286

[357] X. Luo et al. Nonlinear impairment compensation using transfer learning-assisted convolutional bidirectional long short-term memory neural network for coherent optical communication systems. Photonics, 9, 919(2022). https://doi.org/10.3390/photonics9120919

[358] H. Ming et al. Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems. J. Lightwave Technol., 40, 2427-2434(2022). https://doi.org/10.1109/JLT.2022.3141404

[359] M. Cao et al. LSTM attention neural-network-based signal detection for hybrid modulated faster-Than-Nyquist optical wireless communications. Sensors, 22, 8992(2022). https://doi.org/10.3390/s22228992

[360] F. N. Khan et al. Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling. Opt. Express, 23, 30337-30346(2015). https://doi.org/10.1364/OE.23.030337

[361] L. Dou et al. Differential pilots aided in-band OSNR monitor with large nonlinear tolerance(2015).

[362] W. Wang et al. Joint OSNR and interchannel nonlinearity estimation method based on fractional Fourier transform. J. Lightwave Technol., 35, 4497-4506(2017). https://doi.org/10.1109/JLT.2017.2744666

[363] X. Wu et al. Applications of artificial neural networks in optical performance monitoring. J. Lightwave Technol., 27, 3580-3589(2009). https://doi.org/10.1109/JLT.2009.2024435

[364] F. N. Khan et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. Opt. Express, 20, 12422-12431(2012). https://doi.org/10.1364/OE.20.012422

[365] E. Ganesan et al. LSTM-based DWBA prediction for tactile applications in optical access network. Photonics, 10, 37(2022). https://doi.org/10.3390/photonics10010037

[366] C. Wang et al. Long short-term memory neural network (LSTM-NN) enabled accurate optical signal-to-noise ratio (OSNR) monitoring. J. Lightwave Technol., 37, 4140-4146(2019). https://doi.org/10.1109/JLT.2019.2904263

[367] P. Ling, M. Li, W. Guan. Channel-attention-enhanced LSTM neural network decoder and equalizer for RSE-based optical camera communications. Electronics, 11, 1272(2022). https://doi.org/10.3390/electronics11081272

[368] X. Pu et al. Sentiment analysis of online course evaluation based on a new ensemble deep learning mode: evidence from Chinese. Appl. Sci., 11, 11313(2021). https://doi.org/10.3390/app112311313

[369] Y. Zhou et al. An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring. Optik, 273, 170443(2023). https://doi.org/10.1016/j.ijleo.2022.170443

[370] C. Zhang et al. Potential failure cause identification for optical networks using deep learning with an attention mechanism. J. Opt. Commun. Networking, 14, A122-A133(2022). https://doi.org/10.1364/JOCN.438900

[371] C. He et al. Polarisation optics for biomedical and clinical applications: a review. Light Sci. Appl., 10, 194(2021). https://doi.org/10.1038/s41377-021-00639-x

[372] Y. Ni et al. Computational spectropolarimetry with a tunable liquid crystal metasurface. eLight, 2, 23(2022). https://doi.org/10.1186/s43593-022-00032-0

[373] Z. Lin et al. Chip-scale full-Stokes spectropolarimeter in silicon photonic circuits. Photonics Res., 8, 864-874(2020). https://doi.org/10.1364/PRJ.385008

[374] S. Yuan et al. Geometric deep optical sensing. Science, 379, eade1220(2023). https://doi.org/10.1126/science.ade1220

[375] C. Ma et al. Intelligent infrared sensing enabled by tunable Moiré quantum geometry. Nature, 604, 266-272(2022). https://doi.org/10.1038/s41586-022-04548-w

[376] M. Wang et al. φ-OTDR pattern recognition based on CNN-LSTM. Optik, 272, 170380(2023). https://doi.org/10.1016/j.ijleo.2022.170380

[377] Q. Wang et al. Assessment of heart rate and respiratory rate for perioperative infants based on ELC model. IEEE Sens. J., 21, 13685-13694(2021). https://doi.org/10.1109/JSEN.2021.3071882

[378] M. Sabih, D. K. Vishwakarma. Crowd anomaly detection with LSTMs using optical features and domain knowledge for improved inferring. Vis. Comput., 38, 1719-1730(2021). https://doi.org/10.1007/s00371-021-02100-x

[379] J. Siłka, M. Wieczorek, M. Woźniak. Recurrent neural network model for high-speed train vibration prediction from time series. Neural Comput. Appl., 34, 13305-13318(2022). https://doi.org/10.1007/s00521-022-06949-4

[380] B. Lee, S. Roh, J. Park. Current status of micro- and nano-structured optical fiber sensors. Opt. Fiber Technol., 15, 209-221(2009). https://doi.org/10.1016/j.yofte.2009.02.006

[381] Y. Fan et al. Dispersion-assisted high-dimensional photodetector. Nature, 630, 77-83(2024). https://doi.org/10.1038/s41586-024-07398-w

[382] T. Wang et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photonics, 17, 408-415(2023). https://doi.org/10.1038/s41566-023-01170-8

[383] C. Huang et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat. Electron., 4, 837-844(2021). https://doi.org/10.1038/s41928-021-00661-2

[384] F. Sunny, M. Nikdast, S. Pasricha. RecLight: a recurrent neural network accelerator with integrated silicon photonics, 98-103(2022). https://doi.org/10.1109/ISVLSI54635.2022.00030

[385] K. Sozos et al. High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks. Commun. Eng., 1, 24(2022). https://doi.org/10.1038/s44172-022-00024-5

[386] H. Zhu et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun., 13, 1044(2022). https://doi.org/10.1038/s41467-022-28702-0

[387] D. C. Tzarouchis et al. Mathematical operations and equation solving with reconfigurable metadevices. Light Sci. Appl., 11, 263(2022). https://doi.org/10.1038/s41377-022-00950-1

[388] M. Reck et al. Experimental realization of any discrete unitary operator. Phys. Rev. Lett., 73, 58-61(1994). https://doi.org/10.1103/PhysRevLett.73.58

[389] W. R. Clements et al. Optimal design for universal multiport interferometers. Optica, 3, 1460-1465(2016). https://doi.org/10.1364/OPTICA.3.001460

[390] F. Shokraneh, S. Geoffroy-Gagnon, O. Liboiron-Ladouceur. Towards phase-error-and loss-tolerant programmable MZI-based optical processors for optical neural networks, 1-2(2020). https://doi.org/10.1109/IPC47351.2020.9252466

[391] Y. Tian et al. Scalable and compact photonic neural chip with low learning-capability-loss. Nanophotonics, 11, 329-344(2022). https://doi.org/10.1515/nanoph-2021-0521

[392] M. Prabhu et al. A recurrent Ising machine in a photonic integrated circuit(2019).

[393] D. A. Miller. Self-aligning universal beam coupler. Opt. Express, 21, 6360-6370(2013). https://doi.org/10.1364/OE.21.006360

[394] D. A. Miller. Self-configuring universal linear optical component. Photonics Res., 1, 1-15(2013). https://doi.org/10.1364/PRJ.1.000001

[395] R. Hamerly, S. Bandyopadhyay, D. Englund. Stability of self-configuring large multiport interferometers. Phys. Rev. Appl., 18, 024018(2022). https://doi.org/10.1103/PhysRevApplied.18.024018

[396] D. A. Miller. Setting up meshes of interferometers–reversed local light interference method. Opt. Express, 25, 29233-29248(2017). https://doi.org/10.1364/OE.25.029233

[397] S. Pai et al. Parallel programming of an arbitrary feedforward photonic network. IEEE J. Sel. Top. Quantum Electron., 26, 6100813(2020). https://doi.org/10.1109/JSTQE.2020.2997849

[398] R. Shao, G. Zhang, X. Gong. Generalized robust training scheme using genetic algorithm for optical neural networks with imprecise components. Photonics Res., 10, 1868-1876(2022). https://doi.org/10.1364/PRJ.449570

[399] H. Zhang et al. Efficient on-chip training of optical neural networks using genetic algorithm. ACS Photonics, 8, 1662-1672(2021). https://doi.org/10.1021/acsphotonics.1c00035

[400] G. Cong et al. On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification. Nat. Commun., 13, 3261(2022). https://doi.org/10.1038/s41467-022-30906-3

[401] B. Wu et al. Chip-to-chip optical multimode communication with universal mode processors. PhotoniX, 4, 37(2023). https://doi.org/10.1186/s43074-023-00114-3

[402] S. Bandyopadhyay et al. Single chip photonic deep neural network with accelerated training(2022).

[403] Z. Zheng et al. Dual adaptive training of photonic neural networks. Nat. Mach. Intell., 5, 1119-1129(2023). https://doi.org/10.1038/s42256-023-00723-4

[404] T. W. Hughes et al. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica, 5, 864-871(2018). https://doi.org/10.1364/OPTICA.5.000864

[405] S. Pai et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science, 380, 398-404(2023). https://doi.org/10.1126/science.ade8450

[406] J. Feldmann et al. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021). https://doi.org/10.1038/s41586-020-03070-1

[407] B. Dong et al. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photonics, 17, 1080-1088(2023). https://doi.org/10.1038/s41566-023-01313-x

[408] L. Yang, L. Zhang, R. Ji. On-chip optical matrix-vector multiplier. Proc. SPIE, 8855, 88550F(2013). https://doi.org/10.1117/12.2028585

[409] J. Cheng, H. Zhou, J. Dong. Photonic matrix computing: from fundamentals to applications. Nanomaterials, 11, 1683(2021). https://doi.org/10.3390/nano11071683

[410] Y. Huang et al. Easily scalable photonic tensor core based on tunable units with single internal phase shifters. Laser Photonics Rev., 17, 2300001(2023). https://doi.org/10.1002/lpor.202300001

[411] A. N. Tait et al. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightwave Technol., 32, 4029-4041(2014). https://doi.org/10.1109/JLT.2014.2345652

[412] A. N. Tait et al. Microring weight banks. IEEE J. Sel. Top. Quantum Electron., 22, 312-325(2016). https://doi.org/10.1109/JSTQE.2016.2573583

[413] A. N. Tait et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep., 7, 7430(2017). https://doi.org/10.1038/s41598-017-07754-z

[414] C. Ramey. Silicon photonics for artificial intelligence acceleration: HotChips 32(2020). https://doi.org/10.1109/HCS49909.2020.9220525

[415] M. Y.-S. Fang et al. Design of optical neural networks with component imprecisions. Opt. Express, 27, 14009-14029(2019). https://doi.org/10.1364/OE.27.014009

[416] R. Barak, Y. Ben-Aryeh. Quantum fast Fourier transform and quantum computation by linear optics. J. Opt. Soc. Am. B, 24, 231-240(2007). https://doi.org/10.1364/JOSAB.24.000231

[417] S. Pai et al. Matrix optimization on universal unitary photonic devices. Phys. Rev. Appl., 11, 064044(2019). https://doi.org/10.1103/PhysRevApplied.11.064044

[418] Y. Liu et al. Reduce footprints of multiport interferometers by cosine-sine-decomposition unfolding(2022).

[419] L. Torrijos-Morán, D. Pérez-Galacho, D. Pérez-López. Silicon programmable photonic circuits based on periodic bimodal waveguides. Laser Photonics Rev., 18, 2300505(2024). https://doi.org/10.1002/lpor.202300505

[420] X. Wang et al. Chip-based high-dimensional optical neural network. Nano-Micro Lett., 14, 221(2022). https://doi.org/10.1007/s40820-022-00957-8

[421] R. Yin et al. Integrated WDM-compatible optical mode division multiplexing neural network accelerator. Optica, 10, 1709-1718(2023). https://doi.org/10.1364/OPTICA.500523

[422] R. Burgwal et al. Using an imperfect photonic network to implement random unitaries. Opt. Express, 25, 28236-28245(2017). https://doi.org/10.1364/OE.25.028236

[423] B. A. Bell, I. A. Walmsley. Further compactifying linear optical unitaries. APL Photonics, 6, 070804(2021). https://doi.org/10.1063/5.0053421

[424] J. A. Neff, R. A. Athale, S. H. Lee. Two-dimensional spatial light modulators: a tutorial. Proc. IEEE, 78, 826-855(1990). https://doi.org/10.1109/5.53402

[425] K. Von Bieren. Lens design for optical Fourier transform systems. Appl. Opt., 10, 2739-2742(1971). https://doi.org/10.1364/AO.10.002739

[426] J. W. Goodman, A. Dias, L. Woody. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett., 2, 1-3(1978). https://doi.org/10.1364/OL.2.000001

[427] R. A. Athale, W. C. Collins. Optical matrix–matrix multiplier based on outer product decomposition. Appl. Opt., 21, 2089-2090(1982). https://doi.org/10.1364/AO.21.002089

[428] B. Wu et al. Programmable integrated photonic coherent matrix: principle, configuring, and applications. Appl. Phys. Rev., 11, 011309(2024). https://doi.org/10.1063/5.0184982

[429] C. Liu et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron., 5, 113-122(2022). https://doi.org/10.1038/s41928-022-00719-9

[430] M. Miscuglio et al. Massively parallel amplitude-only Fourier neural network. Optica, 7, 1812-1819(2020). https://doi.org/10.1364/OPTICA.408659

[431] S. Colburn et al. Optical frontend for a convolutional neural network. Appl. Opt., 58, 3179-3186(2019). https://doi.org/10.1364/AO.58.003179

[432] P. Pad et al. Efficient neural vision systems based on convolutional image acquisition, 12282-12291(2020). https://doi.org/10.1109/CVPR42600.2020.01230

[433] J. Chang et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8, 12324(2018). https://doi.org/10.1038/s41598-018-30619-y

[434] J. W. Goodman, M. E. Cox. Introduction to Fourier optics. Phys. Today, 22, 97-101(1969). https://doi.org/10.1063/1.3035549

[435] L. L. Doskolovich et al. Spatial differentiation of optical beams using phase-shifted Bragg grating. Opt. Lett., 39, 1278-1281(2014). https://doi.org/10.1364/OL.39.001278

[436] Z. Ruan. Spatial mode control of surface plasmon polariton excitation with gain medium: from spatial differentiator to integrator. Opt. Lett., 40, 601-604(2015). https://doi.org/10.1364/OL.40.000601

[437] T. Zhu et al. Plasmonic computing of spatial differentiation. Nat. Commun., 8, 15391(2017). https://doi.org/10.1038/ncomms15391

[438] Z. Dong et al. Optical spatial differentiator based on subwavelength high-contrast gratings. Appl. Phys. Lett., 112, 181102(2018). https://doi.org/10.1063/1.5026309

[439] Y. Fang, Z. Ruan. Optical spatial differentiator for a synthetic three-dimensional optical field. Opt. Lett., 43, 5893-5896(2018). https://doi.org/10.1364/OL.43.005893

[440] Y. Fang, Y. Lou, Z. Ruan. On-grating graphene surface plasmons enabling spatial differentiation in the terahertz region. Opt. Lett., 42, 3840-3843(2017). https://doi.org/10.1364/OL.42.003840

[441] F. Zangeneh-Nejad, A. Khavasi. Spatial integration by a dielectric slab and its planar graphene-based counterpart. Opt. Lett., 42, 1954-1957(2017). https://doi.org/10.1364/OL.42.001954

[442] N. V. Golovastikov et al. Spatial optical integrator based on phase-shifted Bragg gratings. Opt. Commun., 338, 457-460(2015). https://doi.org/10.1016/j.optcom.2014.11.007

[443] W. Shi et al. LOEN: lensless opto-electronic neural network empowered machine vision. Light Sci. Appl., 11, 121(2022). https://doi.org/10.1038/s41377-022-00809-5

[444] W. Shi et al. Lensless opto-electronic neural network architecture for processing multi-color-channel signals, 1-4(2023). https://doi.org/10.1109/OECC56963.2023.10209732

[445] H. Zheng et al. Meta-optic accelerators for object classifiers. Sci. Adv., 8, eabo6410(2022). https://doi.org/10.1126/sciadv.abo6410

[446] J. Spall et al. Fully reconfigurable coherent optical vector–matrix multiplication. Opt. Lett., 45, 5752-5755(2020). https://doi.org/10.1364/OL.401675

[447] L. Bernstein et al. Single-shot optical neural network. Sci. Adv., 9, eadg7904(2023). https://doi.org/10.1126/sciadv.adg7904

[448] Z. Chen et al. Deep learning with coherent VCSEL neural networks. Nat. Photonics, 17, 723-730(2023). https://doi.org/10.1038/s41566-023-01233-w

[449] R. Hamerly et al. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X, 9, 021032(2019). https://doi.org/10.1103/PhysRevX.9.021032

[450] G. Pan et al. Harnessing the capabilities of VCSELs: unlocking the potential for advanced integrated photonic devices and systems. Light Sci. Appl., 13, 229(2024). https://doi.org/10.1038/s41377-024-01561-8

[451] M. Yildirim et al. Nonlinear processing with linear optics. Nat. Photonics, 18, 1076-1082(2024). https://doi.org/10.1038/s41566-024-01494-z

[452] T. Zhou et al. In situ optical backpropagation training of diffractive optical neural networks. Photonics Res., 8, 940-953(2020). https://doi.org/10.1364/PRJ.389553

[453] L. G. Wright et al. Deep physical neural networks trained with backpropagation. Nature, 601, 549-555(2022). https://doi.org/10.1038/s41586-021-04223-6

[454] J. Spall, X. Guo, A. I. Lvovsky. Hybrid training of optical neural networks. Optica, 9, 803-811(2022). https://doi.org/10.1364/OPTICA.456108

[455] T. Zhou et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367-373(2021). https://doi.org/10.1038/s41566-021-00796-w

[456] O. Kulce et al. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl., 10, 25(2021). https://doi.org/10.1038/s41377-020-00439-9

[457] H. Chen et al. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021). https://doi.org/10.1016/j.eng.2020.07.032

[458] M. S. S. Rahman et al. Universal linear intensity transformations using spatially incoherent diffractive processors. Light Sci. Appl., 12, 195(2023). https://doi.org/10.1038/s41377-023-01234-y

[459] J. Li et al. Spectrally encoded single-pixel machine vision using diffractive networks. Sci. Adv., 7, eabd7690(2021). https://doi.org/10.1126/sciadv.abd7690

[460] M. S. S. Rahman et al. Ensemble learning of diffractive optical networks. Light Sci. Appl., 10, 14(2021). https://doi.org/10.1038/s41377-020-00446-w

[461] X. Yang et al. Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks. Adv. Photonics Nexus, 3, 016010(2024). https://doi.org/10.1117/1.APN.3.1.016010

[462] L. Lu et al. Miniaturized diffraction grating design and processing for deep neural network. IEEE Photonics Technol. Lett., 31, 1952-1955(2019). https://doi.org/10.1109/LPT.2019.2948626

[463] E. Goi et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl., 10, 40(2021). https://doi.org/10.1038/s41377-021-00483-z

[464] X. Luo et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci. Appl., 11, 158(2022). https://doi.org/10.1038/s41377-022-00844-2

[465] Z. Wang et al. On-chip wavefront shaping with dielectric metasurface. Nat. Commun., 10, 3547(2019). https://doi.org/10.1038/s41467-019-11578-y

[466] T. Fu et al. Photonic machine learning with on-chip diffractive optics. Nat. Commun., 14, 70(2023). https://doi.org/10.1038/s41467-022-35772-7

[467] Y. Qu et al. Inverse design of an integrated-nanophotonics optical neural network. Sci. Bull., 65, 1177-1183(2020). https://doi.org/10.1016/j.scib.2020.03.042

[468] V. Nikkhah et al. Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication. Nat. Photonics, 18, 501-508(2024). https://doi.org/10.1038/s41566-024-01394-2

[469] W. Liu et al. C-DONN: compact diffractive optical neural network with deep learning regression. Opt. Express, 31, 22127-22143(2023). https://doi.org/10.1364/OE.490072

[470] T. Fu et al. Integrated diffractive optical neural network with space-time interleaving. Chin. Opt. Lett., 21, 091301(2023).

[471] R. Sun et al. Multimode diffractive optical neural network. Adv. Photonics Nexus, 3, 026007(2024). https://doi.org/10.1117/1.APN.3.2.026007

[472] E. Khoram et al. Nanophotonic media for artificial neural inference. Photonics Res., 7, 823-827(2019). https://doi.org/10.1364/PRJ.7.000823

[473] N. Mohammadi Estakhri, B. Edwards, N. Engheta. Inverse-designed metastructures that solve equations. Science, 363, 1333-1338(2019). https://doi.org/10.1126/science.aaw2498

[474] T. Wu et al. Lithography-free reconfigurable integrated photonic processor. Nat. Photonics, 17, 710-716(2023). https://doi.org/10.1038/s41566-023-01205-0

[475] J. Li et al. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci. Appl., 11, 153(2022). https://doi.org/10.1038/s41377-022-00849-x

[476] Z. Duan, H. Chen, X. Lin. Optical multi-task learning using multi-wavelength diffractive deep neural networks. Nanophotonics, 12, 893-903(2023). https://doi.org/10.1515/nanoph-2022-0615

[477] Z. Huang et al. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys. Rev. Appl., 15, 014037(2021). https://doi.org/10.1103/PhysRevApplied.15.014037

[478] Y. Luo et al. Computational imaging without a computer: seeing through random diffusers at the speed of light. eLight, 2, 4(2022). https://doi.org/10.1186/s43593-022-00012-4

[479] J. Hu et al. Subwavelength imaging using a solid-immersion diffractive optical processor. eLight, 4, 8(2024). https://doi.org/10.1186/s43593-024-00067-5

[480] Ç. Işıl et al. Super-resolution image display using diffractive decoders. Sci. Adv., 8, eadd3433(2022). https://doi.org/10.1126/sciadv.add3433

[481] B. Bai et al. All-optical image classification through unknown random diffusers using a single-pixel diffractive network. Light Sci. Appl., 12, 69(2023). https://doi.org/10.1038/s41377-023-01116-3

[482] Y. Chen et al. Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission. Sci. Adv., 9, 8437(2023). https://doi.org/10.1126/sciadv.adf8437

[483] K. Kim et al. Multi-element microscope optimization by a learned sensing network with composite physical layers. Opt. Lett., 45, 5684-5687(2020). https://doi.org/10.1364/OL.401105

[484] A. Liutkus et al. Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep., 4, 5552(2014). https://doi.org/10.1038/srep05552

[485] V. Sitzmann et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graphics, 37, 114(2018). https://doi.org/10.1145/3197517.3201333

[486] E. Markley et al. Physics-based learned diffuser for single-shot 3D imaging(2021).

[487] H. Haim et al. Depth estimation from a single image using deep learned phase coded mask. IEEE Trans. Comput. Imaging, 4, 298-310(2018). https://doi.org/10.1109/TCI.2018.2849326

[488] A. Levin et al. Image and depth from a conventional camera with a coded aperture. ACM Trans. Graphics, 26, 70(2007). https://doi.org/10.1145/1276377.1276464

[489] Y. Chen et al. All-analog photoelectronic chip for high-speed vision tasks. Nature, 623, 48-57(2023). https://doi.org/10.1038/s41586-023-06558-8

[490] Z. Xu et al. Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence. Science, 384, 202-209(2024). https://doi.org/10.1126/science.adl1203

[491] Y. Zuo et al. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019). https://doi.org/10.1364/OPTICA.6.001132

[492] B. J. Shastri et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021). https://doi.org/10.1038/s41566-020-00754-y

[493] A. N. Tait et al. Silicon photonic modulator neuron. Phys. Rev. Appl., 11, 064043(2019). https://doi.org/10.1103/PhysRevApplied.11.064043

[494] X.-Y. Xu et al. A scalable photonic computer solving the subset sum problem. Sci. Adv., 6, eaay5853(2020). https://doi.org/10.1126/sciadv.aay5853

[495] Y. Xie et al. Thermally-reconfigurable silicon photonic devices and circuits. IEEE J. Sel. Top. Quantum Electron., 26, 3600220(2020). https://doi.org/10.1109/JSTQE.2020.3002758

[496] C. Wang et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature, 562, 101-104(2018). https://doi.org/10.1038/s41586-018-0551-y

[497] J. R. Bankwitz et al. Towards ‘smart transceivers’ in FPGA-controlled lithium-niobate-on-insulator integrated circuits for edge computing applications. Opt. Mater. Express, 13, 3667-3676(2023). https://doi.org/10.1364/OME.503340

[498] G. Dabos et al. Neuromorphic photonic technologies and architectures: scaling opportunities and performance frontiers. Opt. Mater. Express, 12, 2343-2367(2022). https://doi.org/10.1364/OME.452138

[499] B. Shi, N. Calabretta, R. Stabile. Deep neural network through an InP SOA-based photonic integrated cross-connect. IEEE J. Sel. Top. Quantum Electron., 26, 7701111(2019). https://doi.org/10.1109/JSTQE.2019.2945548

[500] M. Chen et al. Generic photonic integrated linear operator processor(2023).

[501] T. Tsurugaya et al. Cross-gain modulation-based photonic reservoir computing using low-power-consumption membrane SOA on Si. Opt. Express, 30, 22871-22884(2022). https://doi.org/10.1364/OE.458264

[502] D. Zheng et al. Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA. APL Photonics, 9, 026102(2024). https://doi.org/10.1063/5.0173301

[503] J. Feldmann et al. Integrated 256 cell photonic phase-change memory with 512-bit capacity. IEEE J. Sel. Top. Quantum Electron., 26, 8301807(2019). https://doi.org/10.1109/JSTQE.2019.2956871

[504] X. Chen et al. Neuromorphic photonic memory devices using ultrafast, non-volatile phase-change materials. Adv. Mater., 35, 2203909(2023). https://doi.org/10.1002/adma.202203909

[505] M. Wuttig, H. Bhaskaran, T. Taubner. Phase-change materials for non-volatile photonic applications. Nat. Photonics, 11, 465-476(2017). https://doi.org/10.1038/nphoton.2017.126

[506] W. H. Pernice, H. Bhaskaran. Photonic non-volatile memories using phase change materials. Appl. Phys. Lett., 101, 171101(2012). https://doi.org/10.1063/1.4758996

[507] C. Wu et al. Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films. Sci. Adv., 10, eadk1361(2024). https://doi.org/10.1126/sciadv.adk1361

[508] C. Ríos et al. Ultra-compact nonvolatile phase shifter based on electrically reprogrammable transparent phase change materials. PhotoniX, 3, 26(2022). https://doi.org/10.1186/s43074-022-00070-4

[509] C. Ríos et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photonics, 9, 725-732(2015). https://doi.org/10.1038/nphoton.2015.182

[510] Z. Cheng et al. On-chip photonic synapse. Sci. Adv., 3, e1700160(2017). https://doi.org/10.1126/sciadv.1700160

[511] C. Ríos et al. In-memory computing on a photonic platform. Sci. Adv., 5, eaau5759(2019). https://doi.org/10.1126/sciadv.aau5759

[512] J. Feldmann et al. Calculating with light using a chip-scale all-optical abacus. Nat. Commun., 8, 1256(2017). https://doi.org/10.1038/s41467-017-01506-3

[513] C. Wu et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun., 12, 96(2021). https://doi.org/10.1038/s41467-020-20365-z

[514] X. Li et al. Experimental investigation of silicon and silicon nitride platforms for phase-change photonic in-memory computing. Optica, 7, 218-225(2020). https://doi.org/10.1364/OPTICA.379228

[515] T. Tuma et al. Stochastic phase-change neurons. Nat. Nanotechnol., 11, 693-699(2016). https://doi.org/10.1038/nnano.2016.70

[516] B. Gholipour et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv. Opt. Mater., 3, 635-641(2015). https://doi.org/10.1002/adom.201400472

[517] G. Agnus et al. Two-terminal carbon nanotube programmable devices for adaptive architectures. Adv. Mater., 22, 702-706(2010). https://doi.org/10.1002/adma.200902170

[518] J. Geler-Kremer et al. A non-volatile optical memory in silicon photonics(2021).

[519] P. Freire et al. Artificial neural networks for photonic applications—from algorithms to implementation: tutorial. Adv. Opt. Photonics, 15, 739-834(2023). https://doi.org/10.1364/AOP.484119

[520] Y. Huang et al. Programmable low-threshold optical nonlinear activation functions for photonic neural networks. Opt. Lett., 47, 1810-1813(2022). https://doi.org/10.1364/OL.451287

[521] F. Ashtiani, A. J. Geers, F. Aflatouni. An on-chip photonic deep neural network for image classification. Nature, 606, 501-506(2022). https://doi.org/10.1038/s41586-022-04714-0

[522] I. A. Williamson et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE J. Sel. Top. Quantum Electron., 26, 7700412(2019). https://doi.org/10.1109/JSTQE.2019.2930455

[523] Y. Shi et al. Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks. Nat. Commun., 13, 6048(2022). https://doi.org/10.1038/s41467-022-33877-7

[524] A. Hazan et al. MXene-Nanoflakes-enabled all-optical nonlinear activation function for on-chip photonic deep neural networks. Adv. Mater., 35, 2210216(2023). https://doi.org/10.1002/adma.202210216

[525] B. Dong et al. Turnkey locking of quantum-dot lasers directly grown on Si. Nat. Photonics, 18, 669-676(2024). https://doi.org/10.1038/s41566-024-01413-2

[526] J. Carrasquilla. Machine learning for quantum matter. Adv. Phys.: X, 5, 1797528(2020). https://doi.org/10.1080/23746149.2020.1797528

[527] E. P. Van Nieuwenburg, Y.-H. Liu, S. D. Huber. Learning phase transitions by confusion. Nat. Phys., 13, 435-439(2017). https://doi.org/10.1038/nphys4037

[528] K. Ch’Ng et al. Machine learning phases of strongly correlated fermions. Phys. Rev. X, 7, 031038(2017). https://doi.org/10.1103/PhysRevX.7.031038

[529] P. B. Wigley et al. Fast machine-learning online optimization of ultra-cold-atom experiments. Sci. Rep., 6, 25890(2016). https://doi.org/10.1038/srep25890

[530] L. Cincio et al. Learning the quantum algorithm for state overlap. New J. Phys., 20, 113022(2018). https://doi.org/10.1088/1367-2630/aae94a

[531] P. Rebentrost, M. Mohseni, S. Lloyd. Quantum support vector machine for big data classification. Phys. Rev. Lett., 113, 130503(2014). https://doi.org/10.1103/PhysRevLett.113.130503

[532] S. Lu, S. L. Braunstein. Quantum decision tree classifier. Quantum Inf. Process., 13, 757-770(2014). https://doi.org/10.1007/s11128-013-0687-5

[533] M. Benedetti et al. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol., 4, 043001(2019). https://doi.org/10.1088/2058-9565/ab4eb5

[534] P. Rebentrost et al. Quantum Hopfield neural network. Phys. Rev. A, 98, 042308(2018). https://doi.org/10.1103/PhysRevA.98.042308

[535] V. Dunjko, J. M. Taylor, H. J. Briegel. Quantum-enhanced machine learning. Phys. Rev. Lett., 117, 130501(2016). https://doi.org/10.1103/PhysRevLett.117.130501

[536] A. W. Harrow, A. Hassidim, S. Lloyd. Quantum algorithm for linear systems of equations. Phys. Rev. Lett., 103, 150502(2009). https://doi.org/10.1103/PhysRevLett.103.150502

[537] J. Liu et al. Towards provably efficient quantum algorithms for large-scale machine-learning models. Nat. Commun., 15, 434(2024). https://doi.org/10.1038/s41467-023-43957-x

[538] J. Biamonte et al. Quantum machine learning. Nature, 549, 195-202(2017). https://doi.org/10.1038/nature23474

[539] S. Aaronson. Read the fine print. Nat. Phys., 11, 291-293(2015). https://doi.org/10.1038/nphys3272

[540] K. Bharti et al. Noisy intermediate-scale quantum algorithms. Rev. Mod. Phys., 94, 015004(2022). https://doi.org/10.1103/RevModPhys.94.015004

[541] Z. Li et al. Experimental realization of a quantum support vector machine. Phys. Rev. Lett., 114, 140504(2015). https://doi.org/10.1103/PhysRevLett.114.140504

[542] K. Anai et al. Continuous-variable quantum kernel method on a programmable photonic quantum processor. Phys. Rev. A, 110, 022404(2024). https://doi.org/10.1103/PhysRevA.110.022404

[543] V. Havlíček et al. Supervised learning with quantum-enhanced feature spaces. Nature, 567, 209-212(2019). https://doi.org/10.1038/s41586-019-0980-2

[544] T. Ono et al. Demonstration of a bosonic quantum classifier with data reuploading. Phys. Rev. Lett., 131, 013601(2023). https://doi.org/10.1103/PhysRevLett.131.013601

[545] M. Schuld, N. Killoran. Quantum machine learning in feature Hilbert spaces. Phys. Rev. Lett., 122, 040504(2019). https://doi.org/10.1103/PhysRevLett.122.040504

[546] S. Yu et al. Shedding light on the future: exploring quantum neural networks through optics. Adv. Quantum Technol., 2024, 2400074(2024). https://doi.org/10.1002/qute.202400074

[547] J. Nokkala et al. Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Commun. Phys., 4, 53(2021). https://doi.org/10.1038/s42005-021-00556-w

[548] S. Ghosh, T. Paterek, T. C. Liew. Quantum neuromorphic platform for quantum state preparation. Phys. Rev. Lett., 123, 260404(2019). https://doi.org/10.1103/PhysRevLett.123.260404

[549] P. Mujal et al. Opportunities in quantum reservoir computing and extreme learning machines. Adv. Quantum Technol., 4, 2100027(2021). https://doi.org/10.1002/qute.202100027

[550] A. Pérez-Salinas et al. Data re-uploading for a universal quantum classifier. Quantum, 4, 226(2020). https://doi.org/10.22331/q-2020-02-06-226

[551] H.-Y. Huang, R. Kueng, J. Preskill. Predicting many properties of a quantum system from very few measurements. Nat. Phys., 16, 1050-1057(2020). https://doi.org/10.1038/s41567-020-0932-7

[552] H.-Y. Huang, R. Kueng, J. Preskill. Efficient estimation of Pauli observables by derandomization. Phys. Rev. Lett., 127, 030503(2021). https://doi.org/10.1103/PhysRevLett.127.030503

[553] J. R. McClean et al. Barren plateaus in quantum neural network training landscapes. Nat. Commun., 9, 4812(2018). https://doi.org/10.1038/s41467-018-07090-4

[554] X. Ge, R.-B. Wu, H. Rabitz. The optimization landscape of hybrid quantum–classical algorithms: from quantum control to NISQ applications. Annu. Rev. Control, 54, 314-323(2022). https://doi.org/10.1016/j.arcontrol.2022.06.001

[555] Y. Li. Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network. Light: Adv. Manuf., 4, 206-221(2023).

[556] D. Mengu, A. Ozcan. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater., 10, 2200281(2022). https://doi.org/10.1002/adom.202200281

[557] Y. Rivenson et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl., 8, 23(2019). https://doi.org/10.1038/s41377-019-0129-y

[558] J. Qi et al. Surgical polarimetric endoscopy for the detection of laryngeal cancer. Nat. Biomed. Eng., 7, 971-985(2023). https://doi.org/10.1038/s41551-023-01018-0

[559] N. T. Clancy et al. Multispectral image alignment using a three channel endoscope in vivo during minimally invasive surgery. Biomed. Opt. Express, 3, 2567-2578(2012). https://doi.org/10.1364/BOE.3.002567

[560] N. T. Clancy et al. Multispectral imaging of organ viability during uterine transplantation surgery in rabbits and sheep. J. Biomed. Opt., 21, 106006(2016). https://doi.org/10.1117/1.JBO.21.10.106006

[561] L. Ayala et al. Spectral imaging enables contrast agent–free real-time ischemia monitoring in laparoscopic surgery. Sci. Adv., 9, eadd6778(2023). https://doi.org/10.1126/sciadv.add6778

[562] J. Park et al. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat. Methods, 20, 1645-1660(2023). https://doi.org/10.1038/s41592-023-02041-4

[563] M. E. Kandel et al. Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments. Nat. Commun., 11, 6256(2020). https://doi.org/10.1038/s41467-020-20062-x

[564] Y. Chen et al. Dual polarization modality fusion network for assisting pathological diagnosis. IEEE Trans. Med. Imaging, 42, 304-316(2022). https://doi.org/10.1109/TMI.2022.3210113

[565] B. Duinkerken et al. Automated analysis of ultrastructure through large-scale hyperspectral electron microscopy. NPJ Imaging, 2, 53(2024). https://doi.org/10.1038/s44303-024-00059-7

[566] A. Soker et al. Advancing automated digital pathology by rapid spectral imaging and AI for nuclear segmentation. Opt. Laser Technol., 181, 111988(2025). https://doi.org/10.1016/j.optlastec.2024.111988

[567] C.-L. Lai et al. Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: a comprehensive review. APL Bioeng., 8, 041504(2024). https://doi.org/10.1063/5.0240444

Tools

Get Citation

Copy Citation Text

Fu Feng, Dewang Huo, Ziyang Zhang, Yijie Lou, Shengyao Wang, Zhijuan Gu, Dong-Sheng Liu, Xinhui Duan, Daqian Wang, Xiaowei Liu, Ji Qi, Shaoliang Yu, Qingyang Du, Guangyong Chen, Cuicui Lu, Yu Yu, Xifeng Ren, Xiaocong Yuan, "Symbiotic evolution of photonics and artificial intelligence: a comprehensive review," Adv. Photon. 7, 024001 (2025)

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Reviews

Received: Sep. 8, 2024

Accepted: Jan. 24, 2025

Published Online: Apr. 3, 2025

The Author Email: Qi Ji (ji.qi@zhejianglab.org), Du Qingyang (qydu@zhejianglab.org), Chen Guangyong (gychen@zhejianglab.org), Lu Cuicui (cuicuilu@bit.edu.cn), Yu Yu (yuyu@mail.hust.edu.cn), Ren Xifeng (renxf@ustc.edu.cn), Yuan Xiaocong (xcyuan@zhejianglab.org)

DOI:10.1117/1.AP.7.2.024001

CSTR:32187.14.1.AP.7.2.024001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

微信扫一扫：分享