[1] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015). doi: 10.1038/nature14539
[2] Xiao, X. , Xu, D. & Wan, W. G. Overview: Video recognition from handcrafted method to deep learning method. 2016 International Conference on Audio, Language and Image Processing (ICALIP). Shanghai, China: IEEE, 2016, 646-651.
[3] Zhao, Z. Q. et al. Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems 30, 3212-3232 (2019). doi: 10.1109/TNNLS.2018.2876865
[4] Minaee, S. et al. Image segmentation using deep learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 3523-3542 (2022).
[5] Huang, Z. et al. Pre-sensor computing with compact multilayer optical neural network. Science Advances 10, eado8516 (2024). doi: 10.1126/sciadv.ado8516
[6] Hu, J. T. et al. Diffractive optical computing in free space. Nature Communications 15, 1525 (2024). doi: 10.1038/s41467-024-45982-w
[7] Prucnal, P. R. & Shastri, B. J. Neuromorphic Photonic. (Boca Raton: CRC Press, 2017).
[8] Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004-1008 (2018). doi: 10.1126/science.aat8084
[9] Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light: Science & Applications 8, 112 (2019).
[10] Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39-47 (2020). doi: 10.1038/s41586-020-2973-6
[11] Chen, H. et al. Diffractive deep neural networks at visible wavelengths. Engineering 7, 1483-1491 (2021). doi: 10.1016/j.eng.2020.07.032
[12] Goi, E. et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light: Science & Applications 10, 40 (2021).
[13] Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15, 102-114 (2021). doi: 10.1038/s41566-020-00754-y
[14] Huang, C. R. et al. Prospects and applications of photonic neural networks. Advances in Physics: X 7, 1981155 (2022).
[15] McMahon, P. L. The physics of optical computing. Nature Reviews Physics 5, 717-734 (2023). doi: 10.1038/s42254-023-00645-5
[16] Fu, T. Z. et al. Optical neural networks: progress and challenges. Light: Science & Applications 13, 263 (2024).
[17] Mengu, D. et al. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE Journal of Selected Topics in Quantum Electronics 26, 3700114 (2020).
[18] Bai, B. J. et al. All-optical image classification through unknown random diffusers using a single-pixel diffractive network. Light: Science & Applications 12, 69 (2023).
[19] Goi, E., Schoenhardt, S. & Gu, M. Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks. Nature Communications 13, 7531 (2022). doi: 10.1038/s41467-022-35349-4
[20] Qu, G. Y. et al. All-dielectric metasurface empowered optical-electronic hybrid neural networks. Laser & Photonics Reviews 16, 2100732 (2022). doi: 10.1002/lpor.202100732
[21] Chen, Y. T. et al. All-optical synthesis chip for large-scale intelligent semantic vision generation. Science 390, 1259-1265 (2025). doi: 10.1126/science.adv7434
[22] Wang, H. et al. Toward near-perfect diffractive optical elements via nanoscale 3D printing. ACS Nano 14, 10452-10461 (2020). doi: 10.1021/acsnano.0c04313
[23] Ngo, T. D. et al. Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering 143, 172-196 (2018). doi: 10.1016/j.compositesb.2018.02.012
[24] Mengu, D. et al. Misalignment resilient diffractive optical networks. Nanophotonics 9, 4207-4219 (2020). doi: 10.1515/nanoph-2020-0291
[25] Rahimi, A. & Recht, B. Random features for large-scale kernel machines. Proceedings of the 21st International Conference on Neural Information Processing Systems. Vancouver, Canada: Curran Associates Inc. , 2007, 1177-1184.
[26] Bull, G. , Gao, J. B. & Antolovich, M. Image segmentation using random features. Proceedings of SPIE 9069, Fifth International Conference on Graphic and Image Processing. Hong Kong, China: SPIE, 2014, 90691Z.
[27] Saade, A. et al. Random projections through multiple optical scattering: Approximating kernels at the speed of light. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Shanghai, China: IEEE, 2016, 6215-6219.
[28] Pierangeli, D., Marcucci, G. & Conti, C. Photonic extreme learning machine by free-space optical propagation. Photonics Research 9, 1446-1454 (2021). doi: 10.1364/PRJ.423531
[29] Gigan, S. Imaging and computing with disorder. Nature Physics 18, 980-985 (2022). doi: 10.1038/s41567-022-01681-1
[30] Wang, H. et al. Large-scale photonic computing with nonlinear disordered media. Nature Computational Science 4, 429-439 (2024). doi: 10.1038/s43588-024-00644-1
[31] Luo, M. C. et al. Large-scale artificial intelligence with 41 million nanophotonic neurons on a metasurface. Print at https://arxiv.org/abs/2504.20416 (2025).
[32] Xu, Z. H. et al. Design and analysis of optical extreme learning machine based on free space propagation. Acta Optica Sinica 45, 0320001 (2025). doi: 10.3788/AOS241671
[33] Ouyang, W. Q. et al. Ultrafast 3D nanofabrication via digital holography. Nature Communications 14, 1716 (2023). doi: 10.1038/s41467-023-37163-y
[34] Li, N. X. et al. A progress review on solid-state LiDAR and nanophotonics-based LiDAR sensors. Laser & Photonics Reviews 16, 2100511 (2022). doi: 10.1002/lpor.202100511
[35] Culemann, D., Knuettel, A. & Voges, E. Integrated optical sensor in glass for optical coherence tomography (OCT). IEEE Journal of Selected Topics in Quantum Electronics 6, 730-734 (2000). doi: 10.1109/2944.892611
[36] Pirzada, M. & Altintas, Z. Recent progress in optical sensors for biomedical diagnostics. Micromachines 11, 356 (2020). doi: 10.3390/mi11040356
[37] Xia, F. et al. Nonlinear optical encoding enabled by recurrent linear scattering. Nature Photonics 18, 1067-1075 (2024). doi: 10.1038/s41566-024-01493-0
[38] Saha, S. K. et al. Scalable submicrometer additive manufacturing. Science 366, 105-109 (2019). doi: 10.1126/science.aax8760
[39] Yang, D. et al. Rapid two-photon polymerization of an arbitrary 3D microstructure with 3D focal field engineering. Macromolecular Rapid Communications 40, 1900041 (2019). doi: 10.1002/marc.201900041
[40] Bunea, A. I. et al. Micro 3D printing by two-photon polymerization: Configurations and parameters for the nanoscribe system. Micro 1, 164-180 (2021). doi: 10.3390/micro1020013
[41] Kiefer, P. et al. A multi-photon (7 × 7)-focus 3D laser printer based on a 3D-printed diffractive optical element and a 3D-printed multi-lens array. Light: Advanced Manufacturing 4, 3 (2024). doi: 10.37188/lam.2024.003
[42] Wang, X. E. et al. 3D nanolithography via holographic multi-focus metalens. Laser & Photonics Reviews 18, 2400181 (2024).
[43] Chang, J. L. et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Scientific Reports 8, 12324 (2018). doi: 10.1038/s41598-018-30619-y
[44] Luo, X. H. et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light: Science & Applications 11, 158 (2022).
[45] Zhang, H. Y. et al. Multichannel meta-imagers for accelerating machine vision. Nature Nanotechnology 19, 471-478 (2024). doi: 10.1038/s41565-023-01557-2
[46] Sun, M. M. et al. Modeling of two-photon polymerization in the strong-pulse regime. Additive Manufacturing 60, 103241 (2022). doi: 10.1016/j.addma.2022.103241
[47] Baraniuk, R. et al. A simple proof of the restricted isometry property for random matrices. Constructive Approximation 28, 253-263 (2008). doi: 10.1007/s00365-007-9003-x
[48] Liu, J. M. et al. Directional conversion of a THz propagating wave into surface waves in deformable metagratings. Optics Express 29, 21749-21762 (2021). doi: 10.1364/OE.431817
[49] Lyu, W. et al. Deep-subwavelength gap modes in all-dielectric metasurfaces for high-efficiency and large-angle wavefront bending. Optics Express 30, 12080-12091 (2022). doi: 10.1364/OE.455113
[50] Li, J. X. et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Advanced Photonics 1, 046001 (2019). doi: 10.1117/1.ap.1.4.046001
[51] Duan, Z. Y., Chen, H. & Lin, X. Optical multi-task learning using multi-wavelength diffractive deep neural networks. Nanophotonics 12, 893-903 (2023). doi: 10.1515/nanoph-2022-0615
[52] Zhang, J. J. et al. Advanced image classification using a differential diffractive network with “learned” structured illumination. ACS Photonics 11, 5289-5298 (2024). doi: 10.1021/acsphotonics.4c01511
[53] Zheng, M. J. et al. Diffractive neural networks with improved expressive power for gray-scale image classification. Photonics Research 12, 1159-1166 (2024). doi: 10.1364/PRJ.513845
[54] Blank, M. et al. Actions as space-time shapes. Tenth IEEE International Conference on Computer Vision (ICCV'05). Beijing, China: IEEE, 2005, 1395-1402.
[55] Gorelick, L. et al. Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2247-2253 (2007). doi: 10.1109/TPAMI.2007.70711
[56] He, K. M. et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016, 770-778.
[57] Schraivogel, D. et al. High-speed fluorescence image-enabled cell sorting. Science 375, 315-320 (2022). doi: 10.1126/science.abj3013
[58] LeCun, Y. et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278-2324 (1998). doi: 10.1109/5.726791
[59] Kaggle. Facial Keypoints detection (2013). at https://www.kaggle.com/c/facial-keypoints-detection URL.
[60] Lee, K. C. M. et al. Toward deep biophysical cytometry: prospects and challenges. Trends in Biotechnology 39, 1249-1262 (2021). doi: 10.1016/j.tibtech.2021.03.006
[61] Wang, T. Y. et al. Image sensing with multilayer nonlinear optical neural networks. Nature Photonics 17, 408-415 (2023).
[62] Chen, Y. T. et al. All-analog photoelectronic chip for high-speed vision tasks. Nature 623, 48-57 (2023). doi: 10.1038/s41586-023-06558-8
[63] Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nature Electronics 5, 519-525 (2022). doi: 10.1038/s41928-022-00819-6
[64] Wang, T. Y. et al. Reconfigurable optoelectronic memristor for in-sensor computing applications. Nano Energy 89, 106291 (2021). doi: 10.1016/j.nanoen.2021.106291
[65] Bong, K. et al. 14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on Haar-like face detector. 2017 IEEE International Solid-State Circuits Conference (ISSCC). San Francisco, CA, USA: IEEE, 2017, 248-249.
[66] Wu, N. F. et al. Intelligent nanophotonics: When machine learning sheds light. eLight 5, 5 (2025). doi: 10.1186/s43593-025-00085-x
[67] Rumi, M. et al. Structure–property relationships for two-photon absorbing chromophores: Bis-donor diphenylpolyene and bis(styryl)benzene derivatives. Journal of the American Chemical Society 122, 9500-9510 (2000). doi: 10.1021/ja994497s