T. Wang et al. , Image sensing with multilayer nonlinear optical neural networks . Nat. Photonics 17 ( 5 ), 408 - 415 ( 2023 ). http://doi.org/10.1038/s41566-023-01170-8 http://doi.org/10.1038/s41566-023-01170-8

Z. Xu et al. , A multichannel optical computing architecture for advanced machine vision . Light Sci. Appl. 11 ( 1 ), 255 ( 2022 ). http://doi.org/10.1038/s41377-022-00945-y http://doi.org/10.1038/s41377-022-00945-y

Achiam, J., et al., Gpt-4 Technical Report. arXiv preprint arXiv:2303.08774 http://arxiv.org/abs/2303.08774 (2023)

D.E. Rumelhart , G.E. Hinton , R.J. Williams , Learning representations by back-propagating errors . Nature 323 ( 6088 ), 533 - 536 ( 1986 ). http://doi.org/10.1038/323533a0 http://doi.org/10.1038/323533a0

H.-D. Block , The perceptron: a model for brain functioning I . Rev. Mod. Phys. 34 ( 1 ), 123 ( 1962 ). http://doi.org/10.1103/RevModPhys.34.123 http://doi.org/10.1103/RevModPhys.34.123

Chellapilla, K.; Puri, S.; Simard, P. In High Performance Convolutional Neural Networks for Document Processing , Tenth international workshop on frontiers in handwriting recognition, Suvisoft: 2006.

Deng, J., et al. In Imagenet: A Large-Scale Hierarchical Image Database , 2009 IEEE Conference on Computer Vision and Pattern Recognition, 20–25 June 2009; 2009; pp 248–255.

Krizhevsky, A.; Sutskever, I.; Hinton, G. E., Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process Syst. 25 (2012)

I.J. Goodfellow et al. , Generative adversarial nets . Adv. Neural. Int. 27 , 2672 - 2680 ( 2014 ).

O. Ronneberger , P. Fischer , Brox T . U-Net: convolutional networks for biomedical image segmentation, medical image computing and computer-assisted intervention—MICCAI 2015, Cham, 2015 234 - 241 ( Springer International Publishing , Cham , 2015 ).

He, K., et al. In Deep Residual Learning for Image Recognition , Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; pp 770–778.

A. Vaswani et al. , Attention is all you need . Adv. Neural. Inf. Process. Syst. 30 , 30 ( 2017 ).

J. Ho , A. Jain , P. Abbeel , Denoising diffusion probabilistic models . Adv. Neural. Inf. Process. Syst. 33 , 6840 - 6851 ( 2020 ).

OpenAI Hello Gpt-4o. https://openai.com/index/hello-gpt-4o/ https://openai.com/index/hello-gpt-4o/ .

R. Soref , J.J. Larenzo , All-silicon active and passive guided-wave components for λ = 1.3 and 1.6 µm . IEEE J. Quantum Electron. 22 ( 6 ), 873 - 879 ( 1986 ). http://doi.org/10.1109/JQE.1986.1073057 http://doi.org/10.1109/JQE.1986.1073057

E. Yablonovitch , T.J. Gmitter , Photonic band structure: the face-centered-cubic case . Phys. Rev. Lett. 63 ( 18 ), 1950 - 1953 ( 1989 ). http://doi.org/10.1103/PhysRevLett.63.1950 http://doi.org/10.1103/PhysRevLett.63.1950

N. Yu et al. , Light propagation with phase discontinuities: generalized laws of reflection and refraction . Science 334 ( 6054 ), 333 - 337 ( 2011 ). http://doi.org/10.1126/science.1210713 http://doi.org/10.1126/science.1210713

A. Arbabi et al. , Dielectric metasurfaces for complete control of phase and polarization with subwavelength spatial resolution and high transmission . Nat. Nanotechnol. 10 ( 11 ), 937 - 943 ( 2015 ). http://doi.org/10.1038/nnano.2015.186 http://doi.org/10.1038/nnano.2015.186

S. Wang et al. , A broadband achromatic metalens in the visible . Nat. Nanotechnol. 13 ( 3 ), 227 - 232 ( 2018 ). http://doi.org/10.1038/s41565-017-0052-4 http://doi.org/10.1038/s41565-017-0052-4

Y. LeCun et al. , Gradient-based learning applied to document recognition . Proc. IEEE 86 ( 11 ), 2278 - 2324 ( 1998 ). http://doi.org/10.1109/5.726791 http://doi.org/10.1109/5.726791

Gu, A.; Dao, T., Mamba: Linear-Time Sequence Modeling with Selective State Spaces. arXiv preprint arXiv:2312.00752 http://arxiv.org/abs/2312.00752 (2023)

Liu, Z., et al., Kan: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756 http://arxiv.org/abs/2404.19756 (2024)

Hui, T.-W.; Tang, X.; Loy, C. C. In Liteflownet: A Lightweight Convolutional Neural Network for Optical Flow Estimation , Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp 8981–8989.

Kim, K.-H., et al., Pvanet: Deep but Lightweight Neural Networks for Real-Time Object Detection. arXiv preprint arXiv:1608.08021 http://arxiv.org/abs/1608.08021 (2016)

D. Colladon , On the reflections of a ray of light inside a parabolic liquid stream . Comptes Rendus 15 ( 800–802 ), 15 ( 1842 ).

L. Mach , Ueber Einen Interferenzrefraktor . Zeitschrift für Instrumentenkunde 12 ( 3 ), 89 ( 1892 ).

G. Cai et al. , Compact angle-resolved metasurface spectrometer . Nat. Mater. 23 ( 1 ), 71 - 78 ( 2024 ). http://doi.org/10.1038/s41563-023-01710-1 http://doi.org/10.1038/s41563-023-01710-1

A. Arbabi et al. , Miniature optical planar camera based on a wide-angle metasurface doublet corrected for monochromatic aberrations . Nat. Commun. 7 ( 1 ), 1 - 9 ( 2016 ). http://doi.org/10.1038/ncomms13682 http://doi.org/10.1038/ncomms13682

H. Pahlevaninezhad et al. , Nano-optic endoscope for high-resolution optical coherence tomography in vivo . Nat. Photonics 12 ( 9 ), 540 - 547 ( 2018 ). http://doi.org/10.1038/s41566-018-0224-2 http://doi.org/10.1038/s41566-018-0224-2

H. Kwon et al. , Single-shot quantitative phase gradient microscopy using a system of multifunctional metasurfaces . Nat. Photonics 14 ( 2 ), 109 - 114 ( 2020 ). http://doi.org/10.1038/s41566-019-0536-x http://doi.org/10.1038/s41566-019-0536-x

F. Yesilkoy et al. , Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces . Nat. Photonics 13 ( 6 ), 390 - 396 ( 2019 ). http://doi.org/10.1038/s41566-019-0394-6 http://doi.org/10.1038/s41566-019-0394-6

E. Arbabi et al. , Full-stokes imaging polarimetry using dielectric metasurfaces . ACS Photonics 5 ( 8 ), 3132 - 3140 ( 2018 ). http://doi.org/10.1021/acsphotonics.8b00362 http://doi.org/10.1021/acsphotonics.8b00362

T. Phan et al. , High-efficiency, large-area, topology-optimized metasurfaces . Light Sci. Appl. 8 ( 1 ), 48 ( 2019 ). http://doi.org/10.1038/s41377-019-0159-5 http://doi.org/10.1038/s41377-019-0159-5

W. Ma , F. Cheng , Y. Liu , Deep-learning-enabled on-demand design of chiral metamaterials . ACS Nano 12 ( 6 ), 6326 - 6334 ( 2018 ). http://doi.org/10.1021/acsnano.8b03569 http://doi.org/10.1021/acsnano.8b03569

W. Ma et al. , Deep learning for the design of photonic structures . Nat. Photonics 15 ( 2 ), 77 - 90 ( 2021 ). http://doi.org/10.1038/s41566-020-0685-y http://doi.org/10.1038/s41566-020-0685-y

Y. Xu et al. , Software-defined nanophotonic devices and systems empowered by machine learning . Progr. Quant. Electron 89 , 100469 ( 2023 ). http://doi.org/10.1016/j.pquantelec.2023.100469 http://doi.org/10.1016/j.pquantelec.2023.100469

Y. Rivenson et al. , Phase recovery and holographic image reconstruction using deep learning in neural networks . Light Sci. Appl. 7 ( 2 ), 17141 - 17141 ( 2018 ). http://doi.org/10.1038/lsa.2017.141 http://doi.org/10.1038/lsa.2017.141

C.-S. Ho et al. , Rapid identification of pathogenic bacteria using raman spectroscopy and deep learning . Nat. Commun. 10 ( 1 ), 1 - 8 ( 2019 ). http://doi.org/10.1038/s41467-019-12898-9 http://doi.org/10.1038/s41467-019-12898-9

G. Barbastathis , A. Ozcan , G. Situ , On the use of deep learning for computational imaging . Optica 6 ( 8 ), 921 - 943 ( 2019 ). http://doi.org/10.1364/OPTICA.6.000921 http://doi.org/10.1364/OPTICA.6.000921

C. Zuo et al. , Deep learning in optical metrology: a review . Light Sci. Appl. 11 ( 1 ), 39 ( 2022 ). http://doi.org/10.1038/s41377-022-00714-x http://doi.org/10.1038/s41377-022-00714-x

A. Tsakyridis et al. , Photonic neural networks and optics-informed deep learning fundamentals . APL Photonics 9 ( 1 ),( 2024 ). http://doi.org/10.1063/5.0169810 http://doi.org/10.1063/5.0169810

A. Montes McNeil et al. , Fundamentals and recent developments of free-space optical neural networks . J. Appl. Phys. 136 ( 3 ),( 2024 ). http://doi.org/10.1063/5.0215752 http://doi.org/10.1063/5.0215752

T. Fu et al. , Optical neural networks: progress and challenges . Light Sci. Appl. 13 ( 1 ), 263 ( 2024 ). http://doi.org/10.1038/s41377-024-01590-3 http://doi.org/10.1038/s41377-024-01590-3

Y. Shen et al. , Deep learning with coherent nanophotonic circuits . Nat. Photonics 11 ( 7 ), 441 - 446 ( 2017 ). http://doi.org/10.1038/nphoton.2017.93 http://doi.org/10.1038/nphoton.2017.93

X. Lin et al. , All-optical machine learning using diffractive deep neural networks . Science 361 ( 6406 ), 1004 - 1008 ( 2018 ). http://doi.org/10.1126/science.aat8084 http://doi.org/10.1126/science.aat8084

Z. Xu et al. , Large-scale photonic chiplet taichi empowers 160-tops/W artificial general intelligence . Science 384 ( 6692 ), 202 - 209 ( 2024 ). http://doi.org/10.1126/science.adl1203 http://doi.org/10.1126/science.adl1203

B.J. Shastri et al. , Photonics for artificial intelligence and neuromorphic computing . Nat. Photonics 15 ( 2 ), 102 - 114 ( 2021 ). http://doi.org/10.1038/s41566-020-00754-y http://doi.org/10.1038/s41566-020-00754-y

Szegedy, C., et al. In Going Deeper with Convolutions , 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7–12 June 2015; 2015; pp 1–9.

V. Mnih et al. , Human-level control through deep reinforcement learning . Nature 518 ( 7540 ), 529 - 533 ( 2015 ). http://doi.org/10.1038/nature14236 http://doi.org/10.1038/nature14236

A. Radford et al. , Language models are unsupervised multitask learners . OpenAI Blog 1 ( 8 ), 9 ( 2019 ).

T. Brown et al. , Language models are few-shot learners . Adv. Neural. Inf. Process. Syst. 33 , 1877 - 1901 ( 2020 ).

Y. Bengio et al. , Gflownet foundations . J. Mach. Learn. Res. 24 ( 1 ), 10006 - 10060 ( 2023 ).

Zhu, L., et al., Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model. arXiv preprint arXiv:.09417 (2024)

J. Feldmann et al. , Parallel convolutional processing using an integrated photonic tensor core . Nature 589 ( 7840 ), 52 - 58 ( 2021 ). http://doi.org/10.1038/s41586-020-03070-1 http://doi.org/10.1038/s41586-020-03070-1

T. Zhou et al. , Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit . Nat. Photonics 15 ( 5 ), 367 - 373 ( 2021 ). http://doi.org/10.1038/s41566-021-00796-w http://doi.org/10.1038/s41566-021-00796-w

Y. Chen et al. , All-analog photoelectronic chip for high-speed vision tasks . Nature 623 ( 7985 ), 48 - 57 ( 2023 ). http://doi.org/10.1038/s41586-023-06558-8 http://doi.org/10.1038/s41586-023-06558-8

Z. Huang et al. , All-optical signal processing of vortex beams with diffractive deep neural networks . Phys. Rev. Appl. 15 ( 1 ),( 2021 ). http://doi.org/10.1103/PhysRevApplied.15.014037 http://doi.org/10.1103/PhysRevApplied.15.014037

B. Bai et al. , All-optical image classification through unknown random diffusers using a single-pixel diffractive network . Light Sci. Appl. 12 ( 1 ), 69 ( 2023 ). http://doi.org/10.1038/s41377-023-01116-3 http://doi.org/10.1038/s41377-023-01116-3

D. Mengu , A. Ozcan , All-optical phase recovery: diffractive computing for quantitative phase imaging . Adv. Opt. Mater. 10 ( 15 ), 2200281 ( 2022 ). http://doi.org/10.1002/adom.202200281 http://doi.org/10.1002/adom.202200281

J. Hu et al. , Subwavelength imaging using a solid-immersion diffractive optical processor . eLight 4 ( 1 ), 8 ( 2024 ). http://doi.org/10.1186/s43593-024-00067-5 http://doi.org/10.1186/s43593-024-00067-5

C.-Y. Shen et al. , Multispectral quantitative phase imaging using a diffractive optical network . Adv. Intell. Syst. 5 ( 11 ), 2300300 ( 2023 ). http://doi.org/10.1002/aisy.202300300 http://doi.org/10.1002/aisy.202300300

M. Jang et al. , Relation between speckle decorrelation and optical phase conjugation (Opc)-based turbidity suppression through dynamic scattering media: a study on in vivo mouse skin . Biomed. Opt. Express 6 ( 1 ), 72 - 85 ( 2015 ). http://doi.org/10.1364/BOE.6.000072 http://doi.org/10.1364/BOE.6.000072

N. Ji , D.E. Milkie , E. Betzig , Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues . Nat. Methods 7 ( 2 ), 141 - 147 ( 2010 ). http://doi.org/10.1038/nmeth.1411 http://doi.org/10.1038/nmeth.1411

A.S. Mohammed et al. , The perception system of intelligent ground vehicles in all weather conditions: a systematic literature review . Sensors 20 ( 22 ), 6532 ( 2020 ). http://doi.org/10.3390/s20226532 http://doi.org/10.3390/s20226532

Y. Luo et al., Computational imaging without a computer: seeing through random diffusers at the speed of light. eLight (2022)

T. Tsukada , W. Watanabe , Investigation of image plane for image reconstruction of objects through diffusers via deep learning . J. Biomed. Opt. 27 ( 5 ),( 2022 ). http://doi.org/10.1117/1.JBO.27.5.056001 http://doi.org/10.1117/1.JBO.27.5.056001

D. Mengu et al. , Classification and reconstruction of spatially overlapping phase images using diffractive optical networks . Sci. Rep. 12 ( 1 ), 8446 ( 2022 ). http://doi.org/10.1038/s41598-022-12020-y http://doi.org/10.1038/s41598-022-12020-y

Z. Huang et al. , Orbital angular momentum deep multiplexing holography via an optical diffractive neural network . Opt. Express 30 ( 4 ), 5569 - 5584 ( 2022 ). http://doi.org/10.1364/OE.447337 http://doi.org/10.1364/OE.447337

W. Xiong et al. , Optical diffractive deep neural network-based orbital angular momentum mode add-drop multiplexer . Opt. Express 29 ( 22 ), 36936 - 36952 ( 2021 ). http://doi.org/10.1364/OE.441905 http://doi.org/10.1364/OE.441905

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 ( 1 ), 153 ( 2022 ). http://doi.org/10.1038/s41377-022-00849-x http://doi.org/10.1038/s41377-022-00849-x

C. Qian et al. , Performing optical logic operations by a diffractive neural network . Light Sci. Appl. 9 ( 1 ), 59 ( 2020 ). http://doi.org/10.1038/s41377-020-0303-2 http://doi.org/10.1038/s41377-020-0303-2

X. Ding et al. , Metasurface-based optical logic operators driven by diffractive neural networks . Adv. Mater. 36 ( 9 ), 2308993 ( 2024 ). http://doi.org/10.1002/adma.202308993 http://doi.org/10.1002/adma.202308993

X. Luo et al. , Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible . Light Sci. Appl. 11 ( 1 ), 158 ( 2022 ). http://doi.org/10.1038/s41377-022-00844-2 http://doi.org/10.1038/s41377-022-00844-2

Lu, G., et al., Metasurface-based diffractive optical networks with dual-channel complex amplitude modulation. J Lightwave Technol 1–9 (2024)

J. Li et al. , All-optical complex field imaging using diffractive processors . Light Sci. Appl. 13 ( 1 ), 120 ( 2024 ). http://doi.org/10.1038/s41377-024-01482-6 http://doi.org/10.1038/s41377-024-01482-6

L. Mennel et al. , Ultrafast machine vision with 2D material neural network image sensors . Nature 579 ( 7797 ), 62 - 66 ( 2020 ). http://doi.org/10.1038/s41586-020-2038-x http://doi.org/10.1038/s41586-020-2038-x

J.P. Balthasar Mueller et al. , Metasurface polarization optics: independent phase control of arbitrary orthogonal states of polarization . Phys. Rev. Lett. 118 ( 11 ),( 2017 ). http://doi.org/10.1103/PhysRevLett.118.113901 http://doi.org/10.1103/PhysRevLett.118.113901

M. Faraji-Dana et al. , Compact folded metasurface spectrometer . Nat. Commun. 9 ( 1 ), 4196 ( 2018 ). http://doi.org/10.1038/s41467-018-06495-5 http://doi.org/10.1038/s41467-018-06495-5

W.T. Chen , A.Y. Zhu , F. Capasso , Flat optics with dispersion-engineered metasurfaces . Nat. Rev. Mater. 5 ( 8 ), 604 - 620 ( 2020 ). http://doi.org/10.1038/s41578-020-0203-3 http://doi.org/10.1038/s41578-020-0203-3

A.M. Shaltout , V.M. Shalaev , M.L. Brongersma , Spatiotemporal light control with active metasurfaces . Science 364 ( 6441 ), eaat3100 ( 2019 ). http://doi.org/10.1126/science.aat3100 http://doi.org/10.1126/science.aat3100

B. Wang et al. , Visible-frequency dielectric metasurfaces for multiwavelength achromatic and highly dispersive holograms . Nano Lett. 16 ( 8 ), 5235 - 5240 ( 2016 ). http://doi.org/10.1021/acs.nanolett.6b02326 http://doi.org/10.1021/acs.nanolett.6b02326

W.T. Chen et al. , A broadband achromatic metalens for focusing and imaging in the visible . Nat. Nanotechnol. 13 ( 3 ), 220 - 226 ( 2018 ). http://doi.org/10.1038/s41565-017-0034-6 http://doi.org/10.1038/s41565-017-0034-6

Peng, Y., et al., Neural Holography with Camera-in-the-Loop Training. ACM Trans. Graph. 39 , Article 185 (2020)

Y. Huo et al. , Optical neural network via loose neuron array and functional learning . Nat. Commun. 14 ( 1 ), 2535 ( 2023 ). http://doi.org/10.1038/s41467-023-37390-3 http://doi.org/10.1038/s41467-023-37390-3

Z. Xue et al. , Fully forward mode training for optical neural networks . Nature 632 ( 8024 ), 280 - 286 ( 2024 ). http://doi.org/10.1038/s41586-024-07687-4 http://doi.org/10.1038/s41586-024-07687-4

M.G. Mahmoud et al. , Ai-driven photonics: unleashing the power of ai to disrupt the future of photonics . APL Photonics 9 ( 8 ),( 2024 ). http://doi.org/10.1063/5.0220766 http://doi.org/10.1063/5.0220766

L. Bian et al. , High-resolution single-photon imaging with physics-informed deep learning . Nat. Commun. 14 ( 1 ), 5902 ( 2023 ). http://doi.org/10.1038/s41467-023-41597-9 http://doi.org/10.1038/s41467-023-41597-9

C. Wang et al. , Metasurface-assisted phase-matching-free second harmonic generation in lithium niobate waveguides . Nat. Commun. 8 ( 1 ), 1 - 7 ( 2017 ). http://doi.org/10.1038/s41467-017-02189-6 http://doi.org/10.1038/s41467-017-02189-6

A. Fedotova et al. , Second-harmonic generation in resonant nonlinear metasurfaces based on lithium niobate . Nano Lett. 20 ( 12 ), 8608 - 8614 ( 2020 ). http://doi.org/10.1021/acs.nanolett.0c03290 http://doi.org/10.1021/acs.nanolett.0c03290

M. Haase , H. Schafer , Upconverting Nanoparticles . Angew. Chem. Int. Ed. Engl. 50 ( 26 ), 5808 - 5829 ( 2011 ). http://doi.org/10.1002/anie.201005159 http://doi.org/10.1002/anie.201005159

H.-Y. Luan et al. , Reconfigurable moiré nanolaser arrays with phase synchronization . Nature 624 ( 7991 ), 282 - 288 ( 2023 ). http://doi.org/10.1038/s41586-023-06789-9 http://doi.org/10.1038/s41586-023-06789-9

Xia, F., et al., Nonlinear Optical Encoding Enabled by Recurrent Linear Scattering. Nat. Photonics (2024)

M. Yildirim et al. , Nonlinear processing with linear optics . Nat. Photonics 18 ( 10 ), 1076 - 1082 ( 2024 ). http://doi.org/10.1038/s41566-024-01494-z http://doi.org/10.1038/s41566-024-01494-z

Y. Li , J. Li , A. Ozcan , Nonlinear encoding in diffractive information processing using linear optical materials . Light Sci. Appl. 13 ( 1 ), 173 ( 2024 ). http://doi.org/10.1038/s41377-024-01529-8 http://doi.org/10.1038/s41377-024-01529-8

B. Bai et al. , To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects . eLight 2 ( 1 ), 14 ( 2022 ). http://doi.org/10.1186/s43593-022-00021-3 http://doi.org/10.1186/s43593-022-00021-3

M. Suzuki , C.M.A. Pennartz , J. Aru , How deep is the brain? the shallow brain hypothesis . Nat. Rev. Neurosci. 24 ( 12 ), 778 - 791 ( 2023 ). http://doi.org/10.1038/s41583-023-00756-z http://doi.org/10.1038/s41583-023-00756-z

P. Dong et al. , Silicon photonic devices and integrated circuits . Nanophotonics 3 ( 4–5 ), 215 - 228 ( 2014 ). http://doi.org/10.1515/nanoph-2013-0023 http://doi.org/10.1515/nanoph-2013-0023

W. Bogaerts et al. , Programmable photonic circuits . Nature 586 ( 7828 ), 207 - 216 ( 2020 ). http://doi.org/10.1038/s41586-020-2764-0 http://doi.org/10.1038/s41586-020-2764-0

R. Soref , The past, present, and future of silicon photonics . IEEE J. Sel. Top. Quantum Electron. 12 ( 6 ), 1678 - 1687 ( 2006 ). http://doi.org/10.1109/JSTQE.2006.883151 http://doi.org/10.1109/JSTQE.2006.883151

Kominato, T., et al. In Extremely Low-Loss (0.3 Db/M) and Long Silica-Based Waveguides with Large Width and Clothoid Curve Connection , Proceedings of ECOC, 2004; pp 5–9.

K.K. Lee et al. , Fabrication of ultralow-loss Si/SiO 2 waveguides by roughness reduction . Opt. Lett. 26 ( 23 ), 1888 - 1890 ( 2001 ). http://doi.org/10.1364/OL.26.001888 http://doi.org/10.1364/OL.26.001888

P.T. Lin et al. , Low-stress silicon nitride platform for mid-infrared broadband and monolithically integrated microphotonics . Adv. Opt. Mater. 1 ( 10 ), 732 - 739 ( 2013 ). http://doi.org/10.1002/adom.201300205 http://doi.org/10.1002/adom.201300205

D. Zhu et al. , Integrated photonics on thin-film lithium niobate . Adv. Opt. Photon. 13 ( 2 ), 242 - 352 ( 2021 ). http://doi.org/10.1364/AOP.411024 http://doi.org/10.1364/AOP.411024

M. Li et al. , Lithium niobate photonic-crystal electro-optic modulator . Nat. Commun. 11 ( 1 ), 4123 ( 2020 ). http://doi.org/10.1038/s41467-020-17950-7 http://doi.org/10.1038/s41467-020-17950-7

Stadler, L. Intel Demonstrates First Fully Integrated Optical I/O Chiplet. https://www.intc.com/news-events/press-releases/detail/1699/intel-demonstrates-first-fully-integrated-optical-io https://www.intc.com/news-events/press-releases/detail/1699/intel-demonstrates-first-fully-integrated-optical-io .

C.-H. Fann, W.-H. L., N. F. Wu, J. Y. Wu, H. Hsia, and Douglas C. H. Yu, Novel Parallel Digital Optical Computing System (Doc) for Generative A.I. In IEEE International Electron Devices Meeting , San Francisco, 2024.

O’Donnell, I., et al. In An Integrated, Low Power, Ultra-Wideband Transceiver Architecture for Low-Rate, Indoor Wireless Systems , IEEE CAS Workshop on wireless communications and networking, Citeseer: 2002.

Y. Li , B. Bakkaloglu , C. Chakrabarti , A system level energy model and energy-quality evaluation for integrated transceiver front-ends . IEEE Trans. VLSI Syst. 15 ( 1 ), 90 - 103 ( 2007 ). http://doi.org/10.1109/TVLSI.2007.891095 http://doi.org/10.1109/TVLSI.2007.891095

D.C.O. Brien et al. , High-speed integrated transceivers for optical wireless . IEEE Commun. Mag. 41 ( 3 ), 58 - 62 ( 2003 ). http://doi.org/10.1109/MCOM.2003.1186546 http://doi.org/10.1109/MCOM.2003.1186546

Tanabe, T., et al., All-optical switches on a silicon chip realized using photonic crystal nanocavities. Appl. Phys. Lett 87 (15), (2005)

Y. Vlasov , W.M.J. Green , F. Xia , High-throughput silicon nanophotonic wavelength-insensitive switch for on-chip optical networks . Nat. Photonics 2 ( 4 ), 242 - 246 ( 2008 ). http://doi.org/10.1038/nphoton.2008.31 http://doi.org/10.1038/nphoton.2008.31

F. Krummenacher , N. Joehl , A 4-MHz cmos continuous-time filter with on-chip automatic tuning . IEEE J. Solid-State Circuits 23 ( 3 ), 750 - 758 ( 1988 ). http://doi.org/10.1109/4.315 http://doi.org/10.1109/4.315

N.C. Harris et al. , Efficient, compact and low loss thermo-optic phase shifter in silicon . Opt. Express 22 ( 9 ), 10487 - 10493 ( 2014 ). http://doi.org/10.1364/OE.22.010487 http://doi.org/10.1364/OE.22.010487

S. Bandyopadhyay et al. , Single-chip photonic deep neural network with forward-only training . Nat. Photonics 18 ( 12 ), 1335 - 1343 ( 2024 ). http://doi.org/10.1038/s41566-024-01567-z http://doi.org/10.1038/s41566-024-01567-z

QANT Q.Ant Photonic Ai Accelerator. https://qant.com/photonic-computing/ https://qant.com/photonic-computing/ .

LIGHTELLIGENCE, Photonic Arithmetic Computing Engine.

W. Bogaerts et al. , Silicon microring resonators . Laser Photonics Rev. 6 ( 1 ), 47 - 73 ( 2012 ). http://doi.org/10.1002/lpor.201100017 http://doi.org/10.1002/lpor.201100017

Huang, C., et al., Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits. APL Photonics 5 (4), (2020)

P. Dong et al. , Low power and compact reconfigurable multiplexing devices based on silicon microring resonators . Opt. Express 18 ( 10 ), 9852 - 9858 ( 2010 ). http://doi.org/10.1364/OE.18.009852 http://doi.org/10.1364/OE.18.009852

T.A. Ibrahim et al. , All-optical switching in a laterally coupled microring resonator by carrier injection . IEEE Photonics Technol. Lett. 15 ( 1 ), 36 - 38 ( 2003 ). http://doi.org/10.1109/LPT.2002.805877 http://doi.org/10.1109/LPT.2002.805877

C. Huang et al. , A silicon photonic-electronic neural network for fibre nonlinearity compensation . Nat. Electron. 4 ( 11 ), 837 - 844 ( 2021 ). http://doi.org/10.1038/s41928-021-00661-2 http://doi.org/10.1038/s41928-021-00661-2

W. Zhang et al. , Silicon microring synapses enable photonic deep learning beyond 9-bit precision . Optica 9 ( 5 ), 579 - 584 ( 2022 ). http://doi.org/10.1364/OPTICA.446100 http://doi.org/10.1364/OPTICA.446100

J. Xiang et al. , All-optical silicon microring spiking neuron . Photon. Res. 10 ( 4 ), 939 - 946 ( 2022 ). http://doi.org/10.1364/PRJ.445954 http://doi.org/10.1364/PRJ.445954

S. Ohno et al. , Si microring resonator crossbar array for on-chip inference and training of the optical neural network . ACS Photonics 9 ( 8 ), 2614 - 2622 ( 2022 ). http://doi.org/10.1021/acsphotonics.1c01777 http://doi.org/10.1021/acsphotonics.1c01777

T. Fu et al. , Photonic machine learning with on-chip diffractive optics . Nat. Commun. 14 ( 1 ), 70 ( 2023 ). http://doi.org/10.1038/s41467-022-35772-7 http://doi.org/10.1038/s41467-022-35772-7

X. Meng et al. , Compact optical convolution processing unit based on multimode interference . Nat. Commun. 14 ( 1 ), 3000 ( 2023 ). http://doi.org/10.1038/s41467-023-38786-x http://doi.org/10.1038/s41467-023-38786-x

H.H. Zhu et al. , Space-efficient optical computing with an integrated chip diffractive neural network . Nat. Commun. 13 ( 1 ), 1044 ( 2022 ). http://doi.org/10.1038/s41467-022-28702-0 http://doi.org/10.1038/s41467-022-28702-0

Simonyan, K.; Zisserman, A. J. a. p. a., Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:.13298 (2014)

K. Liu et al. , Integrated nanocavity plasmon light sources for on-chip optical interconnects . ACS Photonics 3 ( 2 ), 233 - 242 ( 2016 ). http://doi.org/10.1021/acsphotonics.5b00476 http://doi.org/10.1021/acsphotonics.5b00476

J.R. Jain et al. , A micromachining-based technology for enhancing germanium light emission via tensile strain . Nat. Photonics 6 ( 6 ), 398 - 405 ( 2012 ). http://doi.org/10.1038/nphoton.2012.111 http://doi.org/10.1038/nphoton.2012.111

T. Hong et al. , A selective-area metal bonding Ingaasp–Si laser . IEEE Photonics Technol. Lett. 22 ( 15 ), 1141 - 1143 ( 2010 ). http://doi.org/10.1109/LPT.2010.2050683 http://doi.org/10.1109/LPT.2010.2050683

Liao, K., et al., Hetero-integrated perovskite/Si 3 N 4 on-chip photonic system. Nat. Photonics (2025)

X. Xu et al. , Photonic perceptron based on a kerr microcomb for high-speed, scalable, optical neural networks . Laser Photonics Rev. 14 ( 10 ), 2000070 ( 2020 ). http://doi.org/10.1002/lpor.202000070 http://doi.org/10.1002/lpor.202000070

Y. Nie et al. , Integrated laser graded neuron enabling high-speed reservoir computing without a feedback loop . Optica 11 ( 12 ), 1690 - 1699 ( 2024 ). http://doi.org/10.1364/OPTICA.537231 http://doi.org/10.1364/OPTICA.537231

D.J. Gauthier et al. , Next generation reservoir computing . Nat. Commun. 12 ( 1 ), 5564 ( 2021 ). http://doi.org/10.1038/s41467-021-25801-2 http://doi.org/10.1038/s41467-021-25801-2

J. Kim et al. , Tunable metasurfaces towards versatile metalenses and metaholograms: a review . Adv. Photonics 4 ( 02 ),( 2022 ). http://doi.org/10.1117/1.AP.4.2.024001 http://doi.org/10.1117/1.AP.4.2.024001

O.A.M. Abdelraouf et al. , Recent advances in tunable metasurfaces: materials, design, and applications . ACS Nano 16 ( 9 ), 13339 - 13369 ( 2022 ). http://doi.org/10.1021/acsnano.2c04628 http://doi.org/10.1021/acsnano.2c04628

M.A. Kats et al. , Ultra-thin perfect absorber employing a tunable phase change material . Appl. Phys. Lett. ( 2012 ). http://doi.org/10.1063/1.4767646 http://doi.org/10.1063/1.4767646

M.R.M. Hashemi et al. , Electronically-controlled beam-steering through vanadium dioxide metasurfaces . Sci. Rep. 6 ( 1 ), 35439 ( 2016 ). http://doi.org/10.1038/srep35439 http://doi.org/10.1038/srep35439

J.C. Martinez et al. , The origin of optical contrast in Sb 2 Te 3 -based phase-change materials . Phys. Status Solidi (B) 257 ( 1 ), 1900289 ( 2020 ). http://doi.org/10.1002/pssb.201900289 http://doi.org/10.1002/pssb.201900289

Y. Wang et al. , Electrical tuning of phase-change antennas and metasurfaces . Nat. Nanotechnol. 16 ( 6 ), 667 - 672 ( 2021 ). http://doi.org/10.1038/s41565-021-00882-8 http://doi.org/10.1038/s41565-021-00882-8

Q. Wang et al. , Optically reconfigurable metasurfaces and photonic devices based on phase change materials . Nat. Photonics 10 ( 1 ), 60 - 65 ( 2016 ). http://doi.org/10.1038/nphoton.2015.247 http://doi.org/10.1038/nphoton.2015.247

C. Choi et al. , Metasurface with nanostructured Ge 2 Sb 2 Te 5 as a platform for broadband-operating wavefront switch . Adv. Opt. Mater. 7 ( 12 ), 1900171 ( 2019 ). http://doi.org/10.1002/adom.201900171 http://doi.org/10.1002/adom.201900171

Z. Zhang et al. , All-optical switch and logic gates based on hybrid silicon-Ge 2 Sb 2 Te 5 metasurfaces . Appl. Opt. 58 ( 27 ), 7392 - 7396 ( 2019 ). http://doi.org/10.1364/AO.58.007392 http://doi.org/10.1364/AO.58.007392

Y. Zhang et al. , Electrically reconfigurable non-volatile metasurface using low-loss optical phase-change material . Nat. Nanotechnol. 16 ( 6 ), 661 - 666 ( 2021 ). http://doi.org/10.1038/s41565-021-00881-9 http://doi.org/10.1038/s41565-021-00881-9

S. Luo et al. , High-throughput computational materials screening and discovery of optoelectronic semiconductors . WIREs Comput. Mol. Sci. 11 ( 1 ),( 2021 ). http://doi.org/10.1002/wcms.1489 http://doi.org/10.1002/wcms.1489

M. Wuttig , H. Bhaskaran , T. Taubner , Phase-change materials for non-volatile photonic applications . Nat. Photonics 11 ( 8 ), 465 - 476 ( 2017 ). http://doi.org/10.1038/nphoton.2017.126 http://doi.org/10.1038/nphoton.2017.126

Y. Zhang et al. , Broadband transparent optical phase change materials for high-performance nonvolatile photonics . Nat. Commun. 10 ( 1 ), 4279 ( 2019 ). http://doi.org/10.1038/s41467-019-12196-4 http://doi.org/10.1038/s41467-019-12196-4

L. Hu et al. , Phase-only liquid-crystal spatial light modulator for wave-front correction with high precision . Opt. Express 12 ( 26 ), 6403 - 6409 ( 2004 ). http://doi.org/10.1364/OPEX.12.006403 http://doi.org/10.1364/OPEX.12.006403

L. Wang , A.M. Urbas , Q. Li , Nature-inspired emerging chiral liquid crystal nanostructures: from molecular self-assembly to DNA mesophase and nanocolloids . Adv. Mater. 32 ( 41 ), 1801335 ( 2020 ). http://doi.org/10.1002/adma.201801335 http://doi.org/10.1002/adma.201801335

Z.-G. Zheng et al. , Three-dimensional control of the helical axis of a chiral nematic liquid crystal by light . Nature 531 ( 7594 ), 352 - 356 ( 2016 ). http://doi.org/10.1038/nature17141 http://doi.org/10.1038/nature17141

K. Ichimura , Photoalignment of liquid-crystal systems . Chem. Rev. 100 ( 5 ), 1847 - 1874 ( 2000 ). http://doi.org/10.1021/cr980079e http://doi.org/10.1021/cr980079e

D. Wang et al. , Color liquid crystal grating based color holographic 3D display system with large viewing angle . Light Sci. Appl. 13 ( 1 ), 16 ( 2024 ). http://doi.org/10.1038/s41377-023-01375-0 http://doi.org/10.1038/s41377-023-01375-0

Hermerschmidt, A., et al., Wave Front Generation Using a Phase-Only Modulating Liquid-Crystal-Based Micro-Display with Hdtv Resolution. In SPIE Optics + Optoelectronics , SPIE: 2007; Vol. 6584, pp 109–118.

Z.-X. Shen et al. , Planar terahertz photonics mediated by liquid crystal polymers . Adv. Opt. Mater. 8 ( 7 ), 1902124 ( 2020 ). http://doi.org/10.1002/adom.201902124 http://doi.org/10.1002/adom.201902124

Lin, X.-w., et al., Self-Polarizing Terahertz Liquid Crystal Phase Shifter. AIP Adv. 1 (3), (2011)

O. Buchnev et al. , Metasurface-based optical liquid crystal cell as an ultrathin spatial phase modulator for Thz applications . ACS Photonics 7 ( 11 ), 3199 - 3206 ( 2020 ). http://doi.org/10.1021/acsphotonics.0c01263 http://doi.org/10.1021/acsphotonics.0c01263

A. Komar et al. , Dynamic beam switching by liquid crystal tunable dielectric metasurfaces . ACS Photonics 5 ( 5 ), 1742 - 1748 ( 2018 ). http://doi.org/10.1021/acsphotonics.7b01343 http://doi.org/10.1021/acsphotonics.7b01343

H.-S. Ee , R. Agarwal , Tunable metasurface and flat optical zoom lens on a stretchable substrate . Nano Lett. 16 ( 4 ), 2818 - 2823 ( 2016 ). http://doi.org/10.1021/acs.nanolett.6b00618 http://doi.org/10.1021/acs.nanolett.6b00618

A. She et al. , Adaptive metalenses with simultaneous electrical control of focal length, astigmatism, and shift . Sci. Adv. 4 ( 2 ), eaap9957 ( 2018 ). http://doi.org/10.1126/sciadv.aap9957 http://doi.org/10.1126/sciadv.aap9957

E. Arbabi et al. , MEMS-tunable dielectric metasurface lens . Nat. Commun. 9 ( 1 ), 812 ( 2018 ). http://doi.org/10.1038/s41467-018-03155-6 http://doi.org/10.1038/s41467-018-03155-6

G. Wetzstein et al. , Inference in artificial intelligence with deep optics and photonics . Nature 588 ( 7836 ), 39 - 47 ( 2020 ). http://doi.org/10.1038/s41586-020-2973-6 http://doi.org/10.1038/s41586-020-2973-6

P.C.V. Thrane et al. , MEMS tunable metasurfaces based on gap plasmon or fabry-pérot resonances . Nano Lett. 22 ( 17 ), 6951 - 6957 ( 2022 ). http://doi.org/10.1021/acs.nanolett.2c01692 http://doi.org/10.1021/acs.nanolett.2c01692

C. Meng et al. , Dynamic piezoelectric MEMS-based optical metasurfaces . Sci. Adv. 7 ( 26 ), eabg5639 ( 2021 ). http://doi.org/10.1126/sciadv.abg5639 http://doi.org/10.1126/sciadv.abg5639

D. Mengu et al. , Misalignment resilient diffractive optical networks . Nanophotonics 9 ( 13 ), 4207 - 4219 ( 2020 ). http://doi.org/10.1515/nanoph-2020-0291 http://doi.org/10.1515/nanoph-2020-0291

G.P. Collins , Kirigami and technology cut a fine figure, together . Proc. Natl. Acad. Sci. 113 ( 2 ), 240 - 241 ( 2016 ). http://doi.org/10.1073/pnas.1523311113 http://doi.org/10.1073/pnas.1523311113

S. Chen et al. , Electromechanically reconfigurable optical nano-kirigami . Nat. Commun. 12 ( 1 ), 1299 ( 2021 ). http://doi.org/10.1038/s41467-021-21565-x http://doi.org/10.1038/s41467-021-21565-x

Z. Liu et al. , Nano-kirigami with giant optical chirality . Sci. Adv. 4 ( 7 ), eaat4436 ( 2018 ). http://doi.org/10.1126/sciadv.aat4436 http://doi.org/10.1126/sciadv.aat4436

S. Chen et al. , Low-loss and broadband 2 × 2 silicon thermo-optic Mach-Zehnder switch with bent directional couplers . Opt. Lett. 41 ( 4 ), 836 - 839 ( 2016 ). http://doi.org/10.1364/OL.41.000836 http://doi.org/10.1364/OL.41.000836

Hughes, T. W., et al. In Training of Photonic Neural Networks through in Situ Backpropagation , 2019 Conference on Lasers and Electro-Optics (CLEO), 5–10 May 2019; 2019; pp 1–2.

S. Pai et al. , Experimentally realized in situ backpropagation for deep learning in photonic neural networks . Science 380 ( 6643 ), 398 - 404 ( 2023 ). http://doi.org/10.1126/science.ade8450 http://doi.org/10.1126/science.ade8450

Z. Zhang et al. , Integrated scanning spectrometer with a tunable micro-ring resonator and an arrayed waveguide grating . Photon. Res. 10 ( 5 ), A74 - A81 ( 2022 ). http://doi.org/10.1364/PRJ.443039 http://doi.org/10.1364/PRJ.443039

W. Zhao et al. , 96-channel on-chip reconfigurable optical add-drop multiplexer for multidimensional multiplexing systems . Nanophotonics 11 ( 18 ), 4299 - 4313 ( 2022 ). http://doi.org/10.1515/nanoph-2022-0319 http://doi.org/10.1515/nanoph-2022-0319

A. Yaacobi et al. , Integrated phased array for wide-angle beam steering . Opt. Lett. 39 ( 15 ), 4575 - 4578 ( 2014 ). http://doi.org/10.1364/OL.39.004575 http://doi.org/10.1364/OL.39.004575

V. Snigirev et al. , Ultrafast tunable lasers using lithium niobate integrated photonics . Nature 615 ( 7952 ), 411 - 417 ( 2023 ). http://doi.org/10.1038/s41586-023-05724-2 http://doi.org/10.1038/s41586-023-05724-2

M. Zhang et al. , Broadband electro-optic frequency comb generation in a lithium niobate microring resonator . Nature 568 ( 7752 ), 373 - 377 ( 2019 ). http://doi.org/10.1038/s41586-019-1008-7 http://doi.org/10.1038/s41586-019-1008-7

Q. Guo et al. , Femtojoule femtosecond all-optical switching in lithium niobate nanophotonics . Nat. Photonics 16 ( 9 ), 625 - 631 ( 2022 ). http://doi.org/10.1038/s41566-022-01044-5 http://doi.org/10.1038/s41566-022-01044-5

M. Xu et al. , High-performance coherent optical modulators based on thin-film lithium niobate platform . Nat. Commun. 11 ( 1 ), 3911 ( 2020 ). http://doi.org/10.1038/s41467-020-17806-0 http://doi.org/10.1038/s41467-020-17806-0

C. Wang et al. , Integrated lithium niobate electro-optic modulators operating at cmos-compatible voltages . Nature 562 ( 7725 ), 101 - 104 ( 2018 ). http://doi.org/10.1038/s41586-018-0551-y http://doi.org/10.1038/s41586-018-0551-y

H. Sun et al. , Recent progress in integrated electro-optic frequency comb generation . J. Semicond. 42 ( 4 ),( 2021 ). http://doi.org/10.1088/1674-4926/42/4/041301 http://doi.org/10.1088/1674-4926/42/4/041301

R.S. Weis , T.K. Gaylord , Lithium niobate: summary of physical properties and crystal structure . Appl. Phys. A 37 ( 4 ), 191 - 203 ( 1985 ). http://doi.org/10.1007/BF00614817 http://doi.org/10.1007/BF00614817

M.G. Suh , K.J. Vahala , Soliton microcomb range measurement . Science 359 ( 6378 ), 884 - 887 ( 2018 ). http://doi.org/10.1126/science.aao1968 http://doi.org/10.1126/science.aao1968

M.G. Suh et al. , Microresonator soliton dual-comb spectroscopy . Science 354 ( 6312 ), 600 - 603 ( 2016 ). http://doi.org/10.1126/science.aah6516 http://doi.org/10.1126/science.aah6516

J. Park et al. , All-solid-state spatial light modulator with independent phase and amplitude control for three-dimensional LiDAR applications . Nat. Nanotechnol. 16 ( 1 ), 69 - 76 ( 2021 ). http://doi.org/10.1038/s41565-020-00787-y http://doi.org/10.1038/s41565-020-00787-y

M. Imran et al. , A survey of optical carrier generation techniques for terabit capacity elastic optical networks . IEEE Commun. Surveys Tutor. 20 ( 1 ), 211 - 263 ( 2018 ). http://doi.org/10.1109/COMST.2017.2775039 http://doi.org/10.1109/COMST.2017.2775039

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

Marinis, L. D.; Contestabile, G.; Andriolli, N. In A Lithium Niobate on Insulator Based Photonic Neural Network , 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), 3–6 July 2022; 2022; pp 1–3.

J. Xiong et al. , Augmented reality and virtual reality displays: emerging technologies and future perspectives . Light Sci. Appl. 10 ( 1 ), 216 ( 2021 ). http://doi.org/10.1038/s41377-021-00658-8 http://doi.org/10.1038/s41377-021-00658-8

Meta The Future of Wearables. https://about.meta.com/realitylabs/orion?tab=Wireless https://about.meta.com/realitylabs/orion?tab=Wireless .

Y. Ding et al. , Waveguide-based augmented reality displays: perspectives and challenges . eLight 3 ( 1 ), 24 ( 2023 ). http://doi.org/10.1186/s43593-023-00057-z http://doi.org/10.1186/s43593-023-00057-z

M. Gopakumar et al. , Full-colour 3D holographic augmented-reality displays with metasurface waveguides . Nature 629 ( 8013 ), 791 - 797 ( 2024 ). http://doi.org/10.1038/s41586-024-07386-0 http://doi.org/10.1038/s41586-024-07386-0

Chen, B., et al., Ultra-thin, ultra-light, rainbow-free AR-glasses based on single-layer full-color SiC diffractive waveguide. arXiv preprint arXiv:.14487 (2024)

Y. Liu et al. , Compact dual-focal augmented reality head-up display using a single picture generation unit with polarization multiplexing . Opt. Express 31 ( 22 ), 35922 - 35936 ( 2023 ). http://doi.org/10.1364/OE.502617 http://doi.org/10.1364/OE.502617

J. Skirnewskaja , T.D. Wilkinson , Automotive holographic head-up displays . Adv. Mater. 34 ( 19 ), 2110463 ( 2022 ). http://doi.org/10.1002/adma.202110463 http://doi.org/10.1002/adma.202110463

M. Yamaguchi , Light-field and holographic three-dimensional displays [Invited] . J. Opt. Soc. Am. A 33 ( 12 ), 2348 - 2364 ( 2016 ). http://doi.org/10.1364/JOSAA.33.002348 http://doi.org/10.1364/JOSAA.33.002348

J. Hua et al. , Foveated glasses-free 3D display with ultrawide field of view via a large-scale 2D-metagrating complex . Light Sci. Appl. 10 ( 1 ), 213 ( 2021 ). http://doi.org/10.1038/s41377-021-00651-1 http://doi.org/10.1038/s41377-021-00651-1

J. Shi et al. , Spatial multiplexing holographic combiner for glasses-free augmented reality . Nanophotonics 9 ( 9 ), 3003 - 3010 ( 2020 ). http://doi.org/10.1515/nanoph-2020-0243 http://doi.org/10.1515/nanoph-2020-0243

D. Wang et al. , Large viewing angle holographic 3D display system based on maximum diffraction modulation . Light Adv. Manu. 4 ( 3 ), 195 - 205 ( 2023 ).

Zhan, T., et al., Augmented reality and virtual reality displays: perspectives and challenges. iScience 23 (8), (2020)

VSTAR Dragon Series Naked-Eye 3D Led Display. https://www.ledvstar.com/product/naked-eye-3d-led-display/ https://www.ledvstar.com/product/naked-eye-3d-led-display/ .

Ledman. 1250m 2 Outdoor Naked-Eye 3D Display Debuts at Pavilion Shopping Center, Ledman Apexls Supporting the Creation of an International Commercial Landmark. https://www.ledman.com/news/1250%E3%8E%A1-outdoor-naked-eye-3d-display-debuts-at-pavilion-shopping-center-ledman-apexls-supporting-the-creation-of-an-international-commercial-landmark.html https://www.ledman.com/news/1250%E3%8E%A1-outdoor-naked-eye-3d-display-debuts-at-pavilion-shopping-center-ledman-apexls-supporting-the-creation-of-an-international-commercial-landmark.html .

Glass, L. Looking Glass 16” Spatial Display. https://lookingglassfactory.com/16-spatial-oled https://lookingglassfactory.com/16-spatial-oled .

SVG TECH GROUP Light Field Imaging Materials. https://svgoptronics.com/en/index.php?route=product/product&product_id=63 https://svgoptronics.com/en/index.php?route=product/product&product_id=63 .

D. Wang et al. , Decimeter-depth and polarization addressable color 3D meta-holography . Nat. Commun. 15 ( 1 ), 8242 ( 2024 ). http://doi.org/10.1038/s41467-024-52267-9 http://doi.org/10.1038/s41467-024-52267-9

D. Blinder et al. , The state-of-the-art in computer generated holography for 3D display . Light Adv. Manuf. 3 ( 3 ), 572 - 600 ( 2022 ).

L. Lesem , P. Hirsch , J. Jordan , The kinoform: a new wavefront reconstruction device . IBM J. Res. Dev. 13 ( 2 ), 150 - 155 ( 1969 ). http://doi.org/10.1147/rd.132.0150 http://doi.org/10.1147/rd.132.0150

B. Brown , A. Lohmann , Computer-generated binary holograms . IBM J. Res. Dev. 13 ( 2 ), 160 - 168 ( 1969 ). http://doi.org/10.1147/rd.132.0160 http://doi.org/10.1147/rd.132.0160

P.-A. Blanche , Holography, and the future of 3D display . Light Adv. Manuf. 2 ( 4 ), 446 - 459 ( 2021 ).

L. Shi et al. , Towards real-time photorealistic 3D holography with deep neural networks . Nature 591 ( 7849 ), 234 - 239 ( 2021 ). http://doi.org/10.1038/s41586-020-03152-0 http://doi.org/10.1038/s41586-020-03152-0

L. Shi , B. Li , W. Matusik , End-to-end learning of 3D phase-only holograms for holographic display . Light Sci. Appl. 11 ( 1 ), 247 ( 2022 ). http://doi.org/10.1038/s41377-022-00894-6 http://doi.org/10.1038/s41377-022-00894-6

Z. Ren , Z. Xu , E.Y. Lam , End-to-end deep learning framework for digital holographic reconstruction . Adv. Photon. 1 ( 1 ),( 2019 ). http://doi.org/10.1117/1.AP.1.1.016004 http://doi.org/10.1117/1.AP.1.1.016004

H. Chen et al. , Efin: enhanced fourier imager network for generalizable autofocusing and pixel super-resolution in holographic imaging . IEEE J. Sel. Top. Quant. Electron. 29 , 1 - 10 ( 2023 ).

L. Huang et al. , Self-supervised learning of hologram reconstruction using physics consistency . Nat. Mach. Intell. 5 ( 8 ), 895 - 907 ( 2023 ). http://doi.org/10.1038/s42256-023-00704-7 http://doi.org/10.1038/s42256-023-00704-7

S. Cao et al. , Dual convolutional neural network for aberration pre-correction and image quality enhancement in integral imaging display . Opt. Express 31 ( 21 ), 34609 - 34625 ( 2023 ). http://doi.org/10.1364/OE.501909 http://doi.org/10.1364/OE.501909

J. Kim , D. Kane , M.S. Banks , The rate of change of vergence-accommodation conflict affects visual discomfort . Vis Res 105 , 159 - 165 ( 2014 ). http://doi.org/10.1016/j.visres.2014.10.021 http://doi.org/10.1016/j.visres.2014.10.021

Z. Zhan et al. , Photonic diffractive generators through sampling noises from scattering media . Nat. Commun. 15 ( 1 ), 10643 ( 2024 ). http://doi.org/10.1038/s41467-024-55058-4 http://doi.org/10.1038/s41467-024-55058-4

Chen, S., et al., Optical Generative Models. arXiv preprint arXiv:.17970 (2024)

Chen, Y. K. In Challenges and Opportunities of Internet of Things , 17th Asia and South Pacific Design Automation Conference, 30 Jan.-2 Feb. 2012; 2012; pp 383–388.

Beckmann, C.; Consolvo, S.; LaMarca, A. In Some Assembly Required: Supporting End-User Sensor Installation in Domestic Ubiquitous Computing Environments , International Conference on Ubiquitous Computing, Springer: 2004; pp 107–124.

B. Redding et al. , Compact spectrometer based on a disordered photonic chip . Nat. Photonics 7 ( 9 ), 746 - 751 ( 2013 ). http://doi.org/10.1038/nphoton.2013.190 http://doi.org/10.1038/nphoton.2013.190

R.J. Lin et al. , Achromatic metalens array for full-colour light-field imaging . Nat. Nanotechnol. 14 ( 3 ), 227 - 231 ( 2019 ). http://doi.org/10.1038/s41565-018-0347-0 http://doi.org/10.1038/s41565-018-0347-0

L. Bian et al. , A broadband hyperspectral image sensor with high spatio-temporal resolution . Nature 635 ( 8037 ), 73 - 81 ( 2024 ). http://doi.org/10.1038/s41586-024-08109-1 http://doi.org/10.1038/s41586-024-08109-1

Y. Fan et al. , Dispersion-assisted high-dimensional photodetector . Nature 630 ( 8015 ), 77 - 83 ( 2024 ). http://doi.org/10.1038/s41586-024-07398-w http://doi.org/10.1038/s41586-024-07398-w

S. Kochanthara et al. , Safety of perception systems for automated driving: a case study on apollo . ACM T Softw. Eng. Meth 33 ( 3 ), 1 - 28 ( 2024 ). http://doi.org/10.1145/3631969 http://doi.org/10.1145/3631969

L. Tan , B. Dong , Research on the development of "Vehicle-Road-Cloud Integration" . Int. J. Mech. Electr. Eng. 3 ( 1 ), 52 - 57 ( 2024 ). http://doi.org/10.62051/ijmee.v3n1.08 http://doi.org/10.62051/ijmee.v3n1.08

Sun, P., et al. In Scalability in Perception for Autonomous Driving: Waymo Open Dataset , Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020; pp 2446–2454.

H. Khayyam et al. , Artificial intelligence and internet of things for autonomous vehicles . Nonlinear approaches in engineering applications: automotive applications of engineering problems 39 - 68 ( Springer International Publishing , Cham , 2020 ). http://doi.org/10.1007/978-3-030-18963-1_2 http://doi.org/10.1007/978-3-030-18963-1_2

Gerla, M., et al. In Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds , 2014 IEEE World Forum on Internet of Things (WF-IoT), 6–8 March 2014; 2014; pp 241–246.

S.-Q. Li et al. , Phase-only transmissive spatial light modulator based on tunable dielectric metasurface . Science 364 ( 6445 ), 1087 - 1090 ( 2019 ). http://doi.org/10.1126/science.aaw6747 http://doi.org/10.1126/science.aaw6747

W. Li et al. , Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision . Nat. Commun. 14 ( 1 ), 989 ( 2023 ). http://doi.org/10.1038/s41467-023-36645-3 http://doi.org/10.1038/s41467-023-36645-3

Technologies, L. Lucidus Technologies. https://lucidus.tech/about/ https://lucidus.tech/about/ .

PIAXAPP LiDAR Applications. https://pixapp.eu/service/lidar-applications/ https://pixapp.eu/service/lidar-applications/ .

Y. Li , J. Ibanez-Guzman , LiDAR for autonomous driving: the principles, challenges, and trends for automotive LiDAR and perception systems . IEEE Signal Process. Mag. 37 ( 4 ), 50 - 61 ( 2020 ). http://doi.org/10.1109/MSP.2020.2973615 http://doi.org/10.1109/MSP.2020.2973615

F.B.P. Malavazi et al. , LiDAR-only based navigation algorithm for an autonomous agricultural robot . Comp. Electron. Agric. 15 , 471 - 479 ( 2018 ).

K. Dorling et al. , Vehicle routing problems for drone delivery . IEEE Trans. Syst. Man Cybern. Syst. 47 ( 1 ), 70 - 85 ( 2017 ). http://doi.org/10.1109/TSMC.2016.2582745 http://doi.org/10.1109/TSMC.2016.2582745

S. Gonzales et al. , Challenges and potential business applications of automated delivery vehicles–a brief overview . JHTT 14 , 908 - 924 ( 2022 ).

H.Y. Jeong , B.D. Song , S. Lee , The flying warehouse delivery system: a quantitative approach for the optimal operation policy of airborne fulfillment center . IEEE Trans. Intell. Transp. Syst. 22 ( 12 ), 7521 - 7530 ( 2021 ). http://doi.org/10.1109/TITS.2020.3003900 http://doi.org/10.1109/TITS.2020.3003900

F. Lu et al. , Order distribution and routing optimization for takeout delivery under drone-rider joint delivery mode . J. Theor. Appl. Electron. Commer. Res. 19 ( 2 ), 774 - 796 ( 2024 ). http://doi.org/10.3390/jtaer19020041 http://doi.org/10.3390/jtaer19020041

J.-P. Aurambout , K. Gkoumas , B. Ciuffo , Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities . Eur. Transp. Res. Rev. 11 ( 1 ), 30 ( 2019 ). http://doi.org/10.1186/s12544-019-0368-2 http://doi.org/10.1186/s12544-019-0368-2

Shen, Z., et al. In A Dynamic Airspace Planning Framework with Ads-B Tracks for Manned and Unmanned Aircraft at Low-Altitude Sharing Airspace, 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), 17-21 Sept. 2017; 2017; pp 1-7

I. Jeelani , M. Gheisari , Safety challenges of uav integration in construction: conceptual analysis and future research roadmap . Saf. Sci. 144 , 105473 ( 2021 ). http://doi.org/10.1016/j.ssci.2021.105473 http://doi.org/10.1016/j.ssci.2021.105473

Mekikis, P. V., et al. In Dynamic Programmable Wireless Environment with Uav-Mounted Static Metasurfaces , 2022 IEEE Conference on Standards for Communications and Networking (CSCN), 28–30 Nov. 2022; 2022; pp 101–104.

T. Adão et al. , Hyperspectral imaging: a review on uav-based sensors, data processing and applications for agriculture and forestry . Remote Sens. 9 ( 11 ), 1110 ( 2017 ). http://doi.org/10.3390/rs9111110 http://doi.org/10.3390/rs9111110

R. Näsi et al. , Using uav-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level . Remote Sens. 7 ( 11 ), 15467 - 15493 ( 2015 ). http://doi.org/10.3390/rs71115467 http://doi.org/10.3390/rs71115467

X. Ge et al. , Combining uav-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring . PeerJ 7 , e6926 ( 2019 ). http://doi.org/10.7717/peerj.6926 http://doi.org/10.7717/peerj.6926

P. Yager , G.J. Domingo , J.J.A.R.B.E. Gerdes , Point-of-care diagnostics for global health . Annu. Rev. Biomed. Eng. 10 ( 1 ), 107 - 144 ( 2008 ). http://doi.org/10.1146/annurev.bioeng.10.061807.160524 http://doi.org/10.1146/annurev.bioeng.10.061807.160524

R. Peeling , D.J. Mabey , Point-of-care tests for diagnosing infections in the developing world . Clin. Microbiol. Infect. 16 ( 8 ), 1062 - 1069 ( 2010 ). http://doi.org/10.1111/j.1469-0691.2010.03279.x http://doi.org/10.1111/j.1469-0691.2010.03279.x

V. Gubala et al. , Point of care diagnostics: status and future . Anal. Chem. 84 ( 2 ), 487 - 515 ( 2012 ). http://doi.org/10.1021/ac2030199 http://doi.org/10.1021/ac2030199

V. Srinivasan , V.K. Pamula , R.B. Fair , An integrated digital microfluidic lab-on-a-chip for clinical diagnostics on human physiological fluids . Lab Chip 4 ( 4 ), 310 - 315 ( 2004 ). http://doi.org/10.1039/b403341h http://doi.org/10.1039/b403341h

W. Xu et al. , Diagnosis and prognosis of myocardial infarction on a plasmonic chip . Nat. Commun. 11 ( 1 ), 1654 ( 2020 ). http://doi.org/10.1038/s41467-020-15487-3 http://doi.org/10.1038/s41467-020-15487-3

C. Tymm et al. , Scalable covid-19 detection enabled by lab-on-chip biosensors . Cell. Mol. Bioeng. 13 ( 4 ), 313 - 329 ( 2020 ). http://doi.org/10.1007/s12195-020-00642-z http://doi.org/10.1007/s12195-020-00642-z

D. Erickson et al. , Smartphone technology can be transformative to the deployment of lab-on-chip diagnostics . Lab Chip 14 ( 17 ), 3159 - 3164 ( 2014 ). http://doi.org/10.1039/C4LC00142G http://doi.org/10.1039/C4LC00142G

I. Navruz et al. , Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array . Lab Chip 13 ( 20 ), 4015 - 4023 ( 2013 ). http://doi.org/10.1039/c3lc50589h http://doi.org/10.1039/c3lc50589h

A. Ozcan , Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools . Lab Chip 14 ( 17 ), 3187 - 3194 ( 2014 ). http://doi.org/10.1039/C4LC00010B http://doi.org/10.1039/C4LC00010B

Y. Jahani et al. , Imaging-based spectrometer-less optofluidic biosensors based on dielectric metasurfaces for detecting extracellular vesicles . Nat. Commun. 12 ( 1 ), 3246 ( 2021 ). http://doi.org/10.1038/s41467-021-23257-y http://doi.org/10.1038/s41467-021-23257-y

E. Razzicchia et al. , Metasurface-enhanced antennas for microwave brain imaging . Diagnostics (Basel) 11 ( 3 ), 424 ( 2021 ). http://doi.org/10.3390/diagnostics11030424 http://doi.org/10.3390/diagnostics11030424

Y. Zhu et al. , Optical conductivity-based ultrasensitive mid-infrared biosensing on a hybrid metasurface . Light Sci. Appl. 7 ( 1 ), 67 ( 2018 ). http://doi.org/10.1038/s41377-018-0066-1 http://doi.org/10.1038/s41377-018-0066-1

M. Iwanaga , All-dielectric metasurface fluorescence biosensors for high-sensitivity antibody/antigen detection . ACS Nano 14 ( 12 ), 17458 - 17467 ( 2020 ). http://doi.org/10.1021/acsnano.0c07722 http://doi.org/10.1021/acsnano.0c07722

M. Iwanaga et al. , Metasurface biosensors enabling single-molecule sensing of cell-free DNA . Nano Lett. 23 ( 12 ), 5755 - 5761 ( 2023 ). http://doi.org/10.1021/acs.nanolett.3c01527 http://doi.org/10.1021/acs.nanolett.3c01527

A. Barulin et al. , Metasurfaces for quantitative biosciences of molecules, cells, and tissues: sensing and diagnostics . ACS Photonics 11 ( 3 ), 904 - 916 ( 2024 ). http://doi.org/10.1021/acsphotonics.3c01576 http://doi.org/10.1021/acsphotonics.3c01576

Liu, S., et al. In Early Diagnosis of Alzheimer's Disease with Deep Learning , 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 29 April-2 May 2014; 2014; pp 1015–1018.

D. Shen , G. Wu , H.-I. Suk , Deep learning in medical image analysis . Ann. Rev. Biomed. Eng. 19 ( 1 ), 221 - 248 ( 2017 ). http://doi.org/10.1146/annurev-bioeng-071516-044442 http://doi.org/10.1146/annurev-bioeng-071516-044442

S. Zare Harofte et al. , Recent advances of utilizing artificial intelligence in lab on a chip for diagnosis and treatment . Small 18 ( 42 ), 2203169 ( 2022 ). http://doi.org/10.1002/smll.202203169 http://doi.org/10.1002/smll.202203169

Y.H. Chia et al. , In vivo intelligent fluorescence endo-microscopy by varifocal meta-device and deep learning . Adv. Sci. 11 ( 20 ), 2307837 ( 2024 ). http://doi.org/10.1002/advs.202307837 http://doi.org/10.1002/advs.202307837

D. Alsaedi , M. El Badawe , O. Ramahi , A breast cancer detection system using metasurfaces with a convolution neural network: a feasibility study . IEEE Trans Microwave Theory Techniq 70 ( 7 ), 3566 - 3576 ( 2022 ). http://doi.org/10.1109/TMTT.2022.3168312 http://doi.org/10.1109/TMTT.2022.3168312

S.K. Patel et al. , Design of encoded graphene-gold metasurface-based circular ring and square sensors for brain tumor detection and optimization using xgboost algorithm . Diamond Related Mater 148 , 111439 ( 2024 ). http://doi.org/10.1016/j.diamond.2024.111439 http://doi.org/10.1016/j.diamond.2024.111439

A. Tittl et al. , Metasurface-based molecular biosensing aided by artificial intelligence . Angew. Chem. Int. Ed. 58 ( 42 ), 14810 - 14822 ( 2019 ). http://doi.org/10.1002/anie.201901443 http://doi.org/10.1002/anie.201901443

A. John-Herpin et al. , Infrared metasurface augmented by deep learning for monitoring dynamics between all major classes of biomolecules . Adv. Mater. 33 ( 14 ), 2006054 ( 2021 ). http://doi.org/10.1002/adma.202006054 http://doi.org/10.1002/adma.202006054

M. Hoque Tania et al. , Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays . Expert Syst. Appl. 139 , 112843 ( 2020 ). http://doi.org/10.1016/j.eswa.2019.112843 http://doi.org/10.1016/j.eswa.2019.112843

N. Jiang et al. , Lateral and vertical flow assays for point-of-care diagnostics . Adv. Healthcare Mater. 8 ( 14 ), 1900244 ( 2019 ). http://doi.org/10.1002/adhm.201900244 http://doi.org/10.1002/adhm.201900244

H.-A. Joung et al. , Paper-based multiplexed vertical flow assay for point-of-care testing . Lab Chip 19 ( 6 ), 1027 - 1034 ( 2019 ). http://doi.org/10.1039/C9LC00011A http://doi.org/10.1039/C9LC00011A

A. Goncharov et al. , Deep learning-enabled multiplexed point-of-care sensor using a paper-based fluorescence vertical flow assay . Small 19 ( 51 ), 2300617 ( 2023 ). http://doi.org/10.1002/smll.202300617 http://doi.org/10.1002/smll.202300617

Z.S. Ballard et al. , Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors . NPJ Digit. Med. 3 ( 1 ), 66 ( 2020 ). http://doi.org/10.1038/s41746-020-0274-y http://doi.org/10.1038/s41746-020-0274-y

R. Ghosh et al. , Rapid single-tier serodiagnosis of lyme disease . Nat. Commun. 15 ( 1 ), 7124 ( 2024 ). http://doi.org/10.1038/s41467-024-51067-5 http://doi.org/10.1038/s41467-024-51067-5

D.C. Mohr , M. Zhang , S.M. Schueller , Personal sensing: understanding mental health using ubiquitous sensors and machine learning . Annu. Rev. Clin. Psychol. 13 , 23 - 47 ( 2017 ). http://doi.org/10.1146/annurev-clinpsy-032816-044949 http://doi.org/10.1146/annurev-clinpsy-032816-044949

E. Garcia-Ceja et al. , Mental health monitoring with multimodal sensing and machine learning: a survey . Pervasive Mob. Comput. 51 , 1 - 26 ( 2018 ). http://doi.org/10.1016/j.pmcj.2018.09.003 http://doi.org/10.1016/j.pmcj.2018.09.003

G.-H. Lee et al. , Multifunctional materials for implantable and wearable photonic healthcare devices . Nat. Rev. Mater. 5 ( 2 ), 149 - 165 ( 2020 ). http://doi.org/10.1038/s41578-019-0167-3 http://doi.org/10.1038/s41578-019-0167-3

Q. Shi et al. , Progress in wearable electronics/photonics—moving toward the era of artificial intelligence and internet of things . InfoMat 2 ( 6 ), 1131 - 1162 ( 2020 ). http://doi.org/10.1002/inf2.12122 http://doi.org/10.1002/inf2.12122

S. Lalmuanawma , J. Hussain , L. Chhakchhuak , Applications of machine learning and artificial intelligence for Covid-19 (Sars-Cov-2) pandemic: a review . Chaos Solitons Fractals 139 , 110059 ( 2020 ). http://doi.org/10.1016/j.chaos.2020.110059 http://doi.org/10.1016/j.chaos.2020.110059

A. Syrowatka et al. , Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases . NPJ Digit Med 4 ( 1 ), 96 ( 2021 ). http://doi.org/10.1038/s41746-021-00459-8 http://doi.org/10.1038/s41746-021-00459-8

G. Ramkumar et al. , IoT-based patient monitoring system for predicting heart disease using deep learning . Measurement 218 , 113235 ( 2023 ). http://doi.org/10.1016/j.measurement.2023.113235 http://doi.org/10.1016/j.measurement.2023.113235

Dunbray, N., et al. An Analytical Survey on Heart Attack Prediction Techniques Based on Machine Learning and IoT , In: Proceeding of International Co nference on Computational Science and Applications, Singapore, 2022; Bhalla, S.; Bedekar, M.; Phalnikar, R.; Sirsikar, S., Eds. Springer Nature Singapore: Singapore, 2022; pp 299–312.

O. Postolache et al. , Remote monitoring of physical rehabilitation of stroke patients using IoT and virtual reality . IEEE J. Sel. Areas Commun. 39 ( 2 ), 562 - 573 ( 2020 ). http://doi.org/10.1109/JSAC.2020.3020600 http://doi.org/10.1109/JSAC.2020.3020600

G. Yang et al. , An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning . IEEE J. Transl. Eng. Health Med. 6 , 1 - 10 ( 2018 ). http://doi.org/10.1109/JTEHM.2018.2879085 http://doi.org/10.1109/JTEHM.2018.2879085

S.C. Malek , H.-S. Ee , R. Agarwal , Strain multiplexed metasurface holograms on a stretchable substrate . Nano Lett. 17 ( 6 ), 3641 - 3645 ( 2017 ). http://doi.org/10.1021/acs.nanolett.7b00807 http://doi.org/10.1021/acs.nanolett.7b00807

A. Yang et al. , Programmable and reversible plasmon mode engineering . Proc. Natl. Acad. Sci. 113 ( 50 ), 14201 - 14206 ( 2016 ). http://doi.org/10.1073/pnas.1615281113 http://doi.org/10.1073/pnas.1615281113

D. Mengu , Y. Rivenson , A. Ozcan , Scale-, shift-, and rotation-invariant diffractive optical networks . ACS Photonics 8 ( 1 ), 324 - 334 ( 2021 ). http://doi.org/10.1021/acsphotonics.0c01583 http://doi.org/10.1021/acsphotonics.0c01583

F. Voigtlaender , The universal approximation theorem for complex-valued neural networks . Appl. Comput. Harmonic Anal. 64 , 33 - 61 ( 2023 ). http://doi.org/10.1016/j.acha.2022.12.002 http://doi.org/10.1016/j.acha.2022.12.002

J.-F. Xing , M.-L. Zheng , X.-M. Duan , Two-photon polymerization microfabrication of hydrogels: an advanced 3D printing technology for tissue engineering and drug delivery . Chem. Soc. Rev. 44 ( 15 ), 5031 - 5039 ( 2015 ). http://doi.org/10.1039/C5CS00278H http://doi.org/10.1039/C5CS00278H

C.R. Ocier et al. , Direct laser writing of volumetric gradient index lenses and waveguides . Light Sci. Appl. 9 ( 1 ), 196 ( 2020 ). http://doi.org/10.1038/s41377-020-00431-3 http://doi.org/10.1038/s41377-020-00431-3

C. Schmutzler , A. Zimmermann , M.F. Zaeh , Compensating warpage of 3D printed parts using free-form deformation . Procedia Cirp 41 , 1017 - 1022 ( 2016 ). http://doi.org/10.1016/j.procir.2015.12.078 http://doi.org/10.1016/j.procir.2015.12.078

X. Wen et al. , 3D-printed silica with nanoscale resolution . Nat. Mater. 20 ( 11 ), 1506 - 1511 ( 2021 ). http://doi.org/10.1038/s41563-021-01111-2 http://doi.org/10.1038/s41563-021-01111-2

J.-S. Park et al. , All-glass, large metalens at visible wavelength using deep-ultraviolet projection lithography . Nano Lett. 19 ( 12 ), 8673 - 8682 ( 2019 ). http://doi.org/10.1021/acs.nanolett.9b03333 http://doi.org/10.1021/acs.nanolett.9b03333

F. Mei et al. , Cascaded metasurfaces for high-purity vortex generation . Nat. Commun. 14 ( 1 ), 6410 ( 2023 ). http://doi.org/10.1038/s41467-023-42137-1 http://doi.org/10.1038/s41467-023-42137-1

L.E. Scriven , Physics and applications of dip coating and spin coating . MRS Online Proc. Libr. 121 ( 1 ), 717 - 729 ( 1988 ). http://doi.org/10.1557/PROC-121-717 http://doi.org/10.1557/PROC-121-717

A. Tarraf et al. , Stress investigation of pecvd dielectric layers for advanced optical MEMS . J. Micromech. Microeng. 14 ( 3 ), 317 ( 2004 ). http://doi.org/10.1088/0960-1317/14/3/001 http://doi.org/10.1088/0960-1317/14/3/001

Y. Zuo et al. , Scalability of all-optical neural networks based on spatial light modulators . Phys. Rev. Appl. 15 ( 5 ),( 2021 ). http://doi.org/10.1103/PhysRevApplied.15.054034 http://doi.org/10.1103/PhysRevApplied.15.054034

F. Zangeneh-Nejad et al. , Analogue computing with metamaterials . Nat. Rev. Mater. 6 ( 3 ), 207 - 225 ( 2021 ). http://doi.org/10.1038/s41578-020-00243-2 http://doi.org/10.1038/s41578-020-00243-2