c hujingtian@hit.edu.cn
p shumin.xiao@hit.edu.cn
q qinghai.song@hit.edu.cn
收稿日期:2025-02-27,
修回日期:2025-03-03,
录用日期:2025-03-04,
网络出版日期:2025-04-11,
纸质出版日期:2025-12
Scan QR Code
Intelligent nanophotonics: when machine learning sheds light[J]. eLight, 2025,5.
Nanfan Wu, Yuxiang Sun, Jingtian Hu, et al. Intelligent nanophotonics: when machine learning sheds light[J]. Elight, 2025, 5.
Intelligent nanophotonics: when machine learning sheds light[J]. eLight, 2025,5. DOI: 10.1186/s43593-025-00085-x.
Nanfan Wu, Yuxiang Sun, Jingtian Hu, et al. Intelligent nanophotonics: when machine learning sheds light[J]. Elight, 2025, 5. DOI: 10.1186/s43593-025-00085-x.
The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research
deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover
optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed
energy efficiency
and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly
we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration.
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
0
浏览量
0
Downloads
0
CSCD
关联资源
相关文章
相关作者
相关机构