The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
All-optical machine learning using diffractive deep neural networks
Open accessAbstract
All-optical deep learning Deep learning uses multilayered artificial neural networks to learn digitally from large datasets. It then performs advanced identification and classification tasks. To date, these multilayered neural networks have been implemented on a computer. Lin et al. demonstrate all-optical machine learning that uses passive optical components that can be patterned and fabricated with 3D-printing. Their hardware approach comprises stacked layers of diffractive optical elements analogous to an artificial neural network that can be trained to execute complex functions at the speed of light. Science , this issue p. 1004
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Essentially, the architecture of a SIM forms an EM neural network (EMNN), which is conceptually similar to an ANN [23], [24].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In [23], Lin et al. designed a passive SIM using 3D printed diffractive layers capable of classifying images of handwritten digits and fashion products at terahertz frequencies.”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As shown in Fig. 1, a SIM is physically fabricated by closely packing multiple layers of programmable metasurfaces, resulting in an architecture that bears some conceptual resemblance to an artificial neural network (ANN) [23].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“According to the Huygens-Fresnel principle [23], each meta-atom on a metasurface layer acts as a secondary source, producing a spherical wave that illuminates all the metaatoms on the next layer.”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [23]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“It also requires significant effort to convert data schemes [36 -39].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [37]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
(2014) · Cited by 33,419
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Flagged first, then by citations
Research works printed but not linked: 4
- C.-Y. Lee P. W. Gallagher Z. Tu in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016) pp. 464–472.
- H. Xiao K. Rasul R. Vollgraf Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747 [cs.LG] (25 August 2017). · arXiv
- S. Zeng B. Zhang J. Gou Deep Collaborative Weight-based Classification. arXiv:1802.07589 [cs.CV] (21 February 2018). · arXiv
- C. Trabelsi et al . Deep complex networks. arXiv:1705.09792 [cs.NE] (27 May 2017). · arXiv
Cited by
Sorted by citations
Items 201–300 of 1,002
Miller, D. A. B. (2023) · Science · doi:10.1126/science.ade3395 · Cited by 100
Ultranarrow-linewidth wavelength-vortex metasurface holography
Meng, W., Fröch, J. E., Cheng, K., Pi, D., Li, B., Majumdar, A., Maier, S. A., Ren, H., Gu, M., & Fang, X. (2025) · Science Advances · doi:10.1126/sciadv.adt9159 · Cited by 100
HolyLight: A Nanophotonic Accelerator for Deep Learning in Data Centers
Liu, W., Liu, W., Ye, Y., Lou, Q., Xie, Y., & Jiang, L. (2019) · Design, Automation, and Test in Europe · doi:10.23919/date.2019.8715195 · Cited by 99
Computational inverse design for cascaded systems of metasurface optics
Backer, A. S. (2019) · Optics Express · doi:10.1364/oe.27.030308 · Cited by 100
Roadmap for unconventional computing with nanotechnology
Finocchio, G., Incorvia, J. A. C., Friedman, J. S., Qu, Y., Giordano, A., Grollier, J., Yang, H., Ciubotaru, F., Chumak, A. V., Naeemi, A., et al. (2024) · Nano Futures · doi:10.1088/2399-1984/ad299a · Cited by 98
Roadmap on Label‐Free Super‐Resolution Imaging
Astratov, V. N., Sahel, Y. B., Eldar, Y. C., Huang, L., Özcan, A., Zheludev, N. I., Zhao, J., Burns, Z., Liu, Z., Narimanov, E. E., et al. (2023) · Laser & Photonics Review · doi:10.1002/lpor.202200029 · Cited by 98
Pan, G., Xun, M., Zhou, X., Sun, Y., Dong, Y., & Wu, D. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01561-8 · Cited by 100
Classification with a disordered dopant-atom network in silicon
Chen, T., van Gelder, J., van de Ven, B., Amitonov, S. V., de Wilde, B., Ruiz Euler, H.-C., Broersma, H., Bobbert, P. A., Zwanenburg, F. A., & van der Wiel, W. G. (2020) · Nature · doi:10.1038/s41586-019-1901-0 · Cited by 95
Photonic neuromorphic information processing and reservoir computing
Lugnan, A., Katumba, A., Laporte, F., Freiberger, M., Sackesyn, S., Ma, C., Gooskens, E., Dambre, J., & Bienstman, P. (2020) · APL Photonics · doi:10.1063/1.5129762 · Cited by 98
Residual D2NN: training diffractive deep neural networks via learnable light shortcuts
Dou, H., Deng, Y. C., Yan, T., Wu, H., Lin, X., & Dai, Q. (2020) · Optics Letters · doi:10.1364/ol.389696 · Cited by 95
Self-citationBackpropagation through nonlinear units for the all-optical training of neural networks
Guo, X., Barrett, T. D., Wang, Z., & Lvovsky, A. I. (2021) · Photonics Research · doi:10.1364/prj.411104 · Cited by 96
Smart computational light microscopes (SCLMs) of smart computational imaging laboratory (SCILab)
Fan, Y., Li, J., Lu, L., Sun, J., Hu, Y., Zhang, J., Li, Z., Shen, Q., Wang, B., Zhang, R., et al. (2021) · PhotoniX · doi:10.1186/s43074-021-00040-2 · Cited by 94
Intelligent meta-imagers: From compressed to learned sensing
Saigre-Tardif, C., Faqiri, R., Zhao, H., Li, L., & del Hougne, P. (2022) · Applied Physics Reviews · doi:10.1063/5.0076022 · Cited by 97
All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging
Mengü, D., & Özcan, A. (2022) · Advanced Optical Materials · doi:10.1002/adom.202200281 · Cited by 94
Two-photon polymerization lithography for imaging optics
Wang, H., Pan, C., Li, C., Menghrajani, K. S., Schmidt, M. A., Li, A., Fan, F., Zhou, Y. W., Zhang, W., Wang, H., et al. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad35fe · Cited by 100
Wu, B., Li, H., Tong, W., Dong, J., & Zhang, X. (2022) · Optical Materials Express · doi:10.1364/ome.447330 · Cited by 93
Deep-Learning Computational Holography: A Review
Shimobaba, T., Blinder, D., Birnbaum, T., Hoshi, I., Shiomi, H., Schelkens, P., & Ito, T. (2022) · Frontiers in Photonics · doi:10.3389/fphot.2022.854391 · Cited by 96
All-optical logic gate computing for high-speed parallel information processing
Jiao, S., Liu, J., Zhang, L., Yu, F., Zuo, G., Zhang, J., Zhao, F., Lin, W., & Shao, L. (2022) · Opto-Electronic Science · doi:10.29026/oes.2022.220010 · Cited by 93
Computer-Free, All-Optical Reconstruction of Holograms Using Diffractive Networks
Rahman, S. S., & Özcan, A. (2021) · ACS Photonics · doi:10.1021/acsphotonics.1c01365 · Cited by 96
Rotational Multiplexing Method Based on Cascaded Metasurface Holography
Wei, Q., Huang, L., Zhao, R., Geng, G., Li, J., Li, X., & Wang, Y. (2022) · Advanced Optical Materials · doi:10.1002/adom.202102166 · Cited by 94
Deep learning-enabled compact optical trigonometric operator with metasurface
Zhao, Z., Wang, Y., Guan, C., Zhang, K., Wu, Q., Li, H., Liu, J., Burokur, S. N., & Ding, X. (2022) · PhotoniX · doi:10.1186/s43074-022-00062-4 · Cited by 92
Roadmap on electromagnetic metamaterials and metasurfaces
Cui, T. J., Zhang, S., Alù, A., Wegener, M., Pendry, S. J., Luo, J., Lai, Y., Wang, Z., Lin, X., Chen, H., et al. (2024) · Journal of Physics Photonics · doi:10.1088/2515-7647/ad1a3b · Cited by 96
Self-citationDeep-learning-powered photonic analog-to-digital conversion
Xu, S., Zou, X., Ma, B., Chen, J., Yu, L., & Zou, W. (2019) · Light Science & Applications · doi:10.1038/s41377-019-0176-4 · Cited by 93
Deep neural network for plasmonic sensor modeling
Li, X., Shu, J., Gu, W., & Gao, L. H. (2019) · Optical Materials Express · doi:10.1364/ome.9.003857 · Cited by 92
Quantum Communications in Future Networks and Services
Manzalini, A. (2020) · Quantum Reports · doi:10.3390/quantum2010014 · Cited by 97
Xu, Y., Zhang, X., Fu, Y., & Liu, Y. (2021) · Photonics Research · doi:10.1364/prj.417693 · Cited by 92
Optical multi‐task learning using multi‐wavelength diffractive deep neural networks
Duan, Z., Chen, H., & Lin, X. (2023) · Nanophotonics · doi:10.1515/nanoph-2022-0615 · Cited by 91
Self-citationAdvances and Challenges of Single‐Pixel Imaging Based on Deep Learning
Song, K., Bian, Y., Wang, D., Li, R., Wu, K., Liu, H., Qin, C., Hu, J., & Xiao, L. (2024) · Laser & Photonics Review · doi:10.1002/lpor.202401397 · Cited by 93
TOPS-speed complex-valued convolutional accelerator for feature extraction and inference
Bai, Y., Xu, Y., Chen, S., Zhu, X., Wang, S., Huang, S., Song, Y., Zheng, Y., Liu, Z., Tan, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55321-8 · Cited by 101
Scale‑, Shift‑, and Rotation-Invariant Diffractive Optical Networks
Mengu, D., Rivenson, Y., & Özcan, A. (2020) · ACS Photonics · doi:10.1021/acsphotonics.0c01583 · Cited by 95
Self-citationInverse design of metasurface optical filters using deep neural network with high degrees of freedom
Xiao, H. M., Fan, Z., Liu, Z., Li, C., & Guo, L. J. (2020) · InfoMat · doi:10.1002/inf2.12116 · Cited by 90
All‐optical ultrafast ReLU function for energy‐efficient nanophotonic deep learning
Li, G. H. Y., Sekine, R., Nehra, R., Gray, R. M., Ledezma, L. M., Guo, Q., & Marandi, A. (2022) · Nanophotonics · doi:10.1515/nanoph-2022-0137 · Cited by 96
Zhu, Y., Zang, X., Chi, H., Zhou, Y., Zhu, Y., & Zhuang, S. (2023) · Light Advanced Manufacturing · doi:10.37188/lam.2023.009 · Cited by 90
Li, J., Gan, T., Bai, B., Luo, Y., Jarrahi, M., & Özcan, A. (2023) · Advanced Photonics · doi:10.1117/1.ap.5.1.016003 · Cited by 94
Self-citationAdvances in information processing and biological imaging using flat optics
Wang, X., Hao, H., He, X., Xie, P., Liu, J., Tan, J., Li, H., Wang, H., Genevet, P., Luo, Y., et al. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00057-2 · Cited by 88
Self-citationInverse design of an integrated-nanophotonics optical neural network
Qu, Y., Zhu, H., Shen, Y., Zhang, J., Tao, C., Ghosh, P., & Qiu, M. (2020) · 中国科学通报：英文版 · doi:10.1016/j.scib.2020.03.042 · Cited by 89
Arbitrary linear transformations for photons in the frequency synthetic dimension
Buddhiraju, S., Dutt, A., Minkov, M., Williamson, I. A. D., & Fan, S. (2021) · Nature Communications · doi:10.1038/s41467-021-22670-7 · Cited by 90
An Electro-Photonic System for Accelerating Deep Neural Networks
Demirkiran, C., Eris, F., Wang, G., Elmhurst, J., Moore, N., Harris, N. C., Basumallik, A., Reddi, V. J., Joshi, A., & Bunandar, D. (2023) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3606949 · Cited by 92
Optical analog computing of spatial differentiation and edge detection with dielectric metasurfaces
Wan, L., Pan, D., Yang, S., Zhang, W., Potapov, A. A., Wu, X. W., Liu, W., Feng, T., & Li, Z. (2020) · Optics Letters · doi:10.1364/ol.386986 · Cited by 87
Feng, C., Gu, J., Zhu, H., Ying, Z., Zhao, Z., Pan, D. Z., & Chen, R. T. (2022) · ACS Photonics · doi:10.1021/acsphotonics.2c01188 · Cited by 90
Photonic and optoelectronic neuromorphic computing
Srouji, L. E., Krishnan, A., Ravichandran, R., Lee, Y., On, M. B., Xiao, X., & Yoo, S. J. B. (2022) · APL Photonics · doi:10.1063/5.0072090 · Cited by 90
Meta-optic accelerators for object classifiers
Zheng, H., Liu, Q., Zhou, Y., Kravchenko, I. I., Huo, Y., & Valentine, J. (2022) · Science Advances · doi:10.1126/sciadv.abo6410 · Cited by 88
Wu, G.-Z., Fang, Y., Wang, Y., Wu, G., & Dai, C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111393 · Cited by 84
Nonlinear co-generation of graphene plasmons for optoelectronic logic operations
Li, Y., An, N., Lu, Z., Wang, Y., Chang, B., Tan, T., Guo, X., Xu, X., He, J., Xia, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30901-8 · Cited by 86
Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks
Guo, X., Xiang, J., Zhang, Y., & Su, Y. (2021) · Advanced Photonics Research · doi:10.1002/adpr.202000212 · Cited by 89
Bai, B., Luo, Y., Gan, T., Hu, J., Li, Y., Zhao, Y., Mengü, D., Jarrahi, M., & Özcan, A. (2022) · eLight · doi:10.1186/s43593-022-00021-3 · Cited by 85
Self-citationA self-biased non-reciprocal magnetic metasurface for bidirectional phase modulation
Yang, W., Qin, J., Long, J., Yan, W., Yang, Y., Li, C., Li, E., Hu, J., Deng, L., Du, Q., et al. (2023) · Nature Electronics · doi:10.1038/s41928-023-00936-w · Cited by 85
Physics-data-driven intelligent optimization for large-aperture metalenses
Ha, Y., Luo, Y., Pu, M., Zhang, F., He, Q., Jin, J., Xu, M., Guo, Y., Li, X., Li, X., et al. (2023) · Opto-Electronic Advances · doi:10.29026/oea.2023.230133 · Cited by 82
Self-citationIntelligent nanophotonics: when machine learning sheds light
Wu, N., Sun, Y., Hu, J., Yang, C., Bai, Z., Wang, F. X., Cui, X., He, S., Li, Y., Zhang, C., et al. (2025) · eLight · doi:10.1186/s43593-025-00085-x · Cited by 91
Applications and Techniques for Fast Machine Learning in Science
Deiana, A. M., Tran, N. V., Agar, J., Blott, M., Guglielmo, G. D., Duarte, J. M., Harris, P. C., Hauck, S., Liu, M., Neubauer, M. S., et al. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.787421 · Cited by 104
Integrated Photonic Neural Networks: Opportunities and Challenges
Liao, K., Dai, T., Yan, Q., Hu, X., & Gong, Q. (2023) · ACS Photonics · doi:10.1021/acsphotonics.2c01516 · Cited by 90
Efficient training and design of photonic neural network through neuroevolution
Zhang, T., Wang, J., Dan, Y., Lanqiu, Y., Dai, J., Han, X., Sun, X., & Xu, K. (2019) · Optics Express · doi:10.1364/oe.27.037150 · Cited by 82
Silva, D. D., Sierla, S., Alahakoon, D., Osipov, E., Yu, X., & Vyatkin, V. (2020) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2019.2952165 · Cited by 86
Freely scalable and reconfigurable optical hardware for deep learning
Bernstein, L., Sludds, A., Hamerly, R., Sze, V., Emer, J., & Englund, D. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-82543-3 · Cited by 90
Stacked Intelligent Metasurface Enabled LEO Satellite Communications Relying on Statistical CSI
Lin, S., An, J., Gan, L., Debbah, M. A., & Yuen, C. (2024) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2024.3368238 · Cited by 80
Stacked Intelligent Metasurfaces for Multiuser Downlink Beamforming in the Wave Domain
An, J., Renzo, M. D., Debbah, M. A., Poor, H. V., & Yuen, C. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3547779 · Cited by 80
Fu, T., Zang, Y., Huang, H., Du, Z., Hu, C., Chen, M., Yang, S., & Chen, H. (2021) · Optics Express · doi:10.1364/oe.435183 · Cited by 79
Real-Time High-Quality Computer-Generated Hologram Using Complex-Valued Convolutional Neural Network
Zhong, C., Sang, X., Yan, B., Li, H., Chen, D., Qin, X., Chen, S., & Ye, X. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3239670 · Cited by 79
Chen, W., Feng, S., Yin, W., Li, Y., Qian, J., Chen, Q., & Zuo, C. (2024) · PhotoniX · doi:10.1186/s43074-024-00139-2 · Cited by 83
Information metasurfaces and intelligent metasurfaces
Ma, Q., Liu, C., Xiao, Q., Gu, Z., Gao, X., Li, L., & Cui, T. J. (2022) · Photonics Insights · doi:10.3788/pi.2022.r01 · Cited by 79
Vector analog computing via on-demand metasurface dispersive polarization transformation
Yang, H., Xu, J., Peng, M., He, H., Jiang, Y., Yu, D., Jin, R., Gu, Y., Hu, Y., Duan, H., et al. (2025) · Science Advances · doi:10.1126/sciadv.adz5123 · Cited by 77
Photonic Matrix Computing: From Fundamentals to Applications
Cheng, J., Zhou, H., & Dong, J. (2021) · Nanomaterials · doi:10.3390/nano11071683 · Cited by 76
Single-shot optical neural network
Bernstein, L., Sludds, A., Panuski, C., Trajtenberg‐Mills, S., Hamerly, R., & Englund, D. R. (2023) · Science Advances · doi:10.1126/sciadv.adg7904 · Cited by 81
Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding
Fang, X., Hu, X., Li, B., Su, H., Cheng, K., Luan, H., & Gu, M. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01386-5 · Cited by 79
Huo, P., Tan, L., Jin, Y., Zhang, Y., Liu, M., Lin, P., Zhang, S., Wang, Y., Ren, H., Lu, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53463-3 · Cited by 76
Stacked Intelligent Metasurfaces for Integrated Sensing and Communications
Niu, H., An, J., Papazafeiropoulos, A., Gan, L., Chatzinotas, S., & Debbah, M. (2024) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2024.3447272 · Cited by 75
Chen, X. Q., Zhang, L., Liu, S., & Cui, T. J. (2022) · Advanced Optical Materials · doi:10.1002/adom.202201900 · Cited by 75
Orbital angular momentum mode logical operation using optical diffractive neural network
Wang, P., Xiong, W., Huang, Z., He, Y., Xie, Z., Liu, J., Ye, H., Li, Y., Fan, D., & Chen, S. (2021) · Photonics Research · doi:10.1364/prj.432919 · Cited by 74
Liu, Y., Huang, M., Chen, Q., & Zhang, D. (2022) · Nature Communications · doi:10.1038/s41467-022-35588-5 · Cited by 74
Bai, B., Li, Y., Luo, Y., Li, X., Çetintaş, E., Jarrahi, M., & Özcan, A. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01116-3 · Cited by 74
Self-citationChao, X., Ran, Q., Chen, J., Li, T., Qian, Q., & Ergu, D. (2022) · International Review of Financial Analysis · doi:10.1016/j.irfa.2022.102023 · Cited by 79
A small microring array that performs large complex-valued matrix-vector multiplication
Cheng, J., Zhao, Y., Zhang, W., Zhou, H., Huang, D., Zhu, Q., Guo, Y., Xu, B., Dong, J., & Zhang, X. (2022) · Frontiers of Optoelectronics · doi:10.1007/s12200-022-00009-4 · Cited by 73
Hybrid training of optical neural networks
Spall, J. C., Guo, X., & Lvovsky, A. I. (2022) · Optica · doi:10.1364/optica.456108 · Cited by 79
Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor
Li, J., Li, X., Yardimci, N. T., Hu, J., Li, Y., Chen, J., Hung, Y.-C., Jarrahi, M., & Özcan, A. (2023) · Nature Communications · doi:10.1038/s41467-023-42554-2 · Cited by 73
Self-citationEmerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications
An, J., Debbah, M., Cui, T. J., Chen, Z. N., & Yuen, C. (2026) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2025.3571069 · Cited by 72
“According to the Huygens–Fresnel principle [23], each meta-atom on a metasurface layer acts as a secondary source, producing a spherical wave that illuminates all the meta-atoms on the next layer.”
InfluentialAcoustic holography using composite metasurfaces
Zhang, J., Tian, Y., Cheng, Y., & Liu, X. (2020) · Applied Physics Letters · doi:10.1063/1.5132629 · Cited by 72
Fully UV Modulated Artificial Synapses with Integrated Sensing, Storage and Computation
Sun, S., Zhang, T., Jin, S., Pan, X., Lü, J., Ye, Z., & Lu, B. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202401403 · Cited by 74
AI meets physics: a comprehensive survey
Jiao, L., Song, X., You, C., Liu, X., Li, L., Chen, P., Tang, X., Feng, Z., Liu, F., Guo, Y., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10874-4 · Cited by 80
Papazafeiropoulos, A., An, J., Kourtessis, P., Ratnarajah, T., & Chatzinotas, S. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3399318 · Cited by 71
Silva, G. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00843 · Cited by 74
Inverse design of acoustic metamaterials based on machine learning using a Gauss–Bayesian model
Zheng, B., Yang, J., Liang, B., & Cheng, J. (2020) · Journal of Applied Physics · doi:10.1063/5.0012392 · Cited by 71
Electromagnetic Metamaterials: From Classical to Quantum
You, J. W., Ma, Q., Zhang, L., Liu, C., Zhang, J., Liu, S., & Cui, T. J. (2023) · Electromagnetic Science · doi:10.23919/emsci.2022.0005 · Cited by 72
Deep Learning‐Assisted Active Metamaterials with Heat‐Enhanced Thermal Transport
Jin, P., Xu, L., Xu, G., Li, J., Qiu, C., & Huang, J. (2023) · Advanced Materials · doi:10.1002/adma.202305791 · Cited by 71
Hu, Y., Song, Y., Zhu, X., Guo, X., Lu, S., Zhang, Q., He, L., Franken, C. A. A., Powell, K. B., Warner, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62635-8 · Cited by 81
Artificial intelligence accelerated by light
Wu, H., & Dai, Q. (2021) · Nature · doi:10.1038/d41586-020-03572-y · Cited by 71
Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks
Xu, X., Tan, M., Wu, J., Boes, A., Corcoran, B., Nguyen, T. G., Chu, S. T., Little, B. E., Morandotti, R., Mitchell, A., et al. (2020) · International Topical Meeting on Microwave Photonics · doi:10.23919/mwp48676.2020.9314409 · Cited by 80
Mengü, D., Rahman, M. S. S., Luo, Y., Li, J., Kulce, O., & Özcan, A. (2022) · Advances in Optics and Photonics · doi:10.1364/aop.450345 · Cited by 71
Self-citationPlasmonic photoconductive terahertz focal-plane array with pixel super-resolution
Li, X., Mengü, D., Yardimci, N. T., Turan, D., Charkhesht, A., Özcan, A., & Jarrahi, M. (2024) · Nature Photonics · doi:10.1038/s41566-023-01346-2 · Cited by 70
Self-citationAll‐in‐one silicon photonic polarization processor
Zhou, H., Zhao, Y., Wei, Y., Li, F., Dong, J., & Zhang, X. (2018) · Nanophotonics · doi:10.1515/nanoph-2019-0310 · Cited by 69
Chen, C., Zhan, Y., Wang, T., Wang, Y., Gao, K., Wu, J., Wang, J., Qiu, J., & Tan, D. (2024) · Nature Communications · doi:10.1038/s41467-024-53371-6 · Cited by 69
All-Optical Spiking Neuron Based on Passive Microresonator
Xiang, J., Torchy, A., Guo, X., & Su, Y. (2020) · Journal of Lightwave Technology · doi:10.1109/jlt.2020.2986233 · Cited by 68
Fang, J., Jin-pei, L., Kong, A., Xie, Y., Lin, C., Xie, Z., Lei, T., & Yuan, X. (2022) · Photonics Research · doi:10.1364/prj.458474 · Cited by 68
Dual adaptive training of photonic neural networks
Zheng, Z., Duan, Z., Chen, H., Yang, R., Gao, S., Zhang, H., Xiong, H., & Lin, X. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00723-4 · Cited by 72
Self-citationIntegrated photonic neural network based on silicon metalines
Zarei, S., Marzban, M. R., & Khavasi, A. (2020) · Optics Express · doi:10.1364/oe.404386 · Cited by 69
Synthetic neuromorphic computing in living cells
Rizik, L., Danial, L., Habib, M., Weiss, R., & Daniel, R. (2022) · Nature Communications · doi:10.1038/s41467-022-33288-8 · Cited by 72
Liu, Z., Gao, S., Lai, Z., Li, Y., Ao, Z., Jin-pei, L., Tu, J., Wu, Y., Liu, W., & Li, Z. (2023) · Laser & Photonics Review · doi:10.1002/lpor.202200536 · Cited by 67
Sunada, S., & Uchida, A. (2021) · Optica · doi:10.1364/optica.434918 · Cited by 66
Orbital angular momentum deep multiplexing holography via an optical diffractive neural network
Huang, Z., He, Y., Wang, P., Xiong, W., Wu, H., Liu, J., Ye, H., Li, Y., Fan, D., & Chen, S. (2022) · Optics Express · doi:10.1364/oe.447337 · Cited by 66
Super-resolution image display using diffractive decoders
Işıl, Ç., Mengü, D., Zhao, Y., Tabassum, A., Li, J., Luo, Y., Jarrahi, M., & Özcan, A. (2022) · Science Advances · doi:10.1126/sciadv.add3433 · Cited by 69
Self-citationDiffractive deep neural networks: Theories, optimization, and applications
Chen, H., Lou, S., Wang, Q., Huang, P., Duan, H., & Hu, Y. (2024) · Applied Physics Reviews · doi:10.1063/5.0191977 · Cited by 70
Metadata
- Authors
- Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A.
- Affiliations
- California NanoSystems Institute (US)
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA.
- Corresponding author
- Lin, Xing
- Rivenson, Yair
- Venue
- Science
- Published
- 2018-09-07
- Volume
- 361
- Issue
- 6406
- Pages
- 1004-1008
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2798701005
- Cited by
- 2,568
- References
- 45
- Citations by year
- 2026: 412 · 2025: 523 · 2024: 448 · 2023: 344 · 2022: 314 · 2021: 241 · 2020: 157 · 2019: 110 · 2018: 19
- FWCI
- 74.25
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Howard Hughes Medical Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Metamaterials and Metasurfaces Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Random lasers and scattering media
- Keywords
- diffractive deep neural networks, diffractive optical elements, optical neural networks, optical computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
