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
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
Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
Parallel convolutional processing using an integrated photonic tensor core
Feldmann, J., Youngblood, N., Karpov, M., Gehring, H., Li, X., Stappers, M., Le Gallo, M., Fu, X., Lukashchuk, A., Raja, A. S., et al. (2021) · Nature · doi:10.1038/s41586-020-03070-1 · Cited by 1,900
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,908
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
11 TOPS photonic convolutional accelerator for optical neural networks
Xu, X., Tan, M., Corcoran, B., Wu, J., Boes, A., Nguyen, T. G., Chu, S. T., Little, B. E., Hicks, D. G., Morandotti, R., et al. (2021) · Nature · doi:10.1038/s41586-020-03063-0 · Cited by 1,573
Physics for neuromorphic computing
Marković, D., Mizrahi, F. A., Querlioz, D., & Grollier, J. (2020) · Emerging Topics in Artificial Intelligence (ETAI) · doi:10.1038/s42254-020-0208-2 · Cited by 1,260
Ultrafast machine vision with 2D material neural network image sensors
Mennel, L., Symonowicz, J., Wachter, S., Polyushkin, D. K., Molina‐Mendoza, A. J., & Mueller, T. (2020) · Nature · doi:10.1038/s41586-020-2038-x · Cited by 1,180
Deep learning for the design of photonic structures
Ma, W., Liu, Z., Kudyshev, Z. A., Boltasseva, A. E., Cai, W., & Liu, Y. (2020) · Nature Photonics · doi:10.1038/s41566-020-0685-y · Cited by 1,180
Inference in artificial intelligence with deep optics and photonics
Wetzstein, G., Özcan, A., Gigan, S., Fan, S., Englund, D. R., Marin Soljacic, A., Denz, C., Miller, D. A. B., & Psaltis, D. (2020) · Nature · doi:10.1038/s41586-020-2973-6 · Cited by 1,092
Orbital Angular Momentum Holography for High-Security Encryption
Fang, X., Ren, H., & Gu, M. (2020) · Nature Photonics · doi:10.1038/s41566-019-0560-x · Cited by 894
Neuromorphic nanoelectronic materials
Sangwan, V. K., & Hersam, M. C. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0647-z · Cited by 907
Complex-amplitude metasurface-based orbital angular momentum holography in momentum space
Ren, H., Fang, X., Jang, J., Bürger, J., Rho, J., & Maier, S. A. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0768-4 · Cited by 788
Deep physical neural networks trained with backpropagation
Wright, L. G., Onodera, T., Stein, M. M., Wang, T., Schachter, D. T., Hu, Z., & McMahon, P. L. (2022) · Nature · doi:10.1038/s41586-021-04223-6 · Cited by 799
The Computational Limits of Deep Learning
Thompson, N., Greenewald, K. H., Lee, K., & Manso, G. F. (2023) · Ninth Computing within Limits 2023 · doi:10.21428/bf6fb269.1f033948 · Cited by 922
An on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Zhou, T., Lin, X., Wu, J., Chen, Y., Xie, H., Li, Y., Fan, J., Wu, H., Fang, L., & Dai, Q. (2021) · Nature Photonics · doi:10.1038/s41566-021-00796-w · Cited by 721
Self-citationAn optical neural chip for implementing complex-valued neural network
Zhang, H., Gu, M., Jiang, X., Thompson, J. E., Cai, H. N., Paesani, S., Santagati, R., Laing, A., Zhang, Y., Yung, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20719-7 · Cited by 688
A programmable diffractive deep neural network based on a digital-coding metasurface array
Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00719-9 · Cited by 633
Metasurface Orbital Angular Momentum Holography
Ren, H., Briere, G., Fang, X., Ni, P., Sawant, R., Héron, S., Chenot, S., Vézian, S., Damilano, B., Brändli, V., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11030-1 · Cited by 599
Deep-learning-enabled self-adaptive microwave cloak without human intervention
Qian, C., Zheng, B., Shen, Y., Jing, L., Li, E., Shen, L., & Chen, H. (2020) · Nature Photonics · doi:10.1038/s41566-020-0604-2 · Cited by 585
Photonic matrix multiplication lights up photonic accelerator and beyond
Zhou, H., Dong, J., Cheng, J., Dong, W., Huang, C., Shen, Y., Zhang, Q., Gu, M., Qian, C., Chen, H., et al. (2022) · Light Science & Applications · doi:10.1038/s41377-022-00717-8 · Cited by 589
Chang, J., Sitzmann, V., Dun, X., Heidrich, W., & Wetzstein, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-30619-y · Cited by 580
Inverse-designed metastructures that solve equations
Estakhri, N. M., Edwards, B. E., & Engheta, N. (2019) · Science · doi:10.1126/science.aaw2498 · Cited by 569
The Computational Limits of Deep Learning
Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020) · arXiv · doi:10.48550/arxiv.2007.05558 · Cited by 538
Deep learning in nano-photonics: inverse design and beyond
Wiecha, P. R., Arbouet, A., Girard, C., & Muskens, O. L. (2021) · Photonics Research · doi:10.1364/prj.415960 · Cited by 549
The rise of intelligent matter
Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021) · Nature · doi:10.1038/s41586-021-03453-y · Cited by 526
Optical meta-waveguides for integrated photonics and beyond
Meng, Y., Chen, Y., Lu, L., Ding, Y., Cusano, A., Fan, J. A., Hu, Q., Wang, K., Xie, Z., Liu, Z., et al. (2021) · Light Science & Applications · doi:10.1038/s41377-021-00655-x · Cited by 487
All-optical neural network with nonlinear activation functions
Zuo, Y., Li, B., Zhao, Y., Jiang, Y., Chen, Y.-C., Chen, P., Jo, G.-B., Liu, J., & Du, S. (2019) · Optica · doi:10.1364/optica.6.001132 · Cited by 479
All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing
Hu, L., Yang, J., Wang, J., Cheng, P., Chua, L. O., & Zhuge, F. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202005582 · Cited by 467
Photonic Perceptron Based on a Kerr Microcomb for High‐Speed, Scalable, Optical Neural Networks
Xu, X., Tan, M., Corcoran, B., Wu, J., Nguyen, T. G., Boes, A., Chu, S. T., Little, B. E., Morandotti, R., Mitchell, A., et al. (2020) · Laser & Photonics Review · doi:10.1002/lpor.202000070 · Cited by 479
Analogue computing with metamaterials
Zangeneh‐Nejad, F., Sounas, D. L., Alù, A., & Fleury, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00243-2 · Cited by 451
A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design
An, S., Fowler, C., Zheng, B., Shalaginov, M. Y., Tang, H., Li, H., Zhou, L. P., Ding, J., Agarwal, A., Rivero, C., et al. (2019) · ACS Photonics · doi:10.1021/acsphotonics.9b00966 · Cited by 427
Machine learning and applications in ultrafast photonics
Genty, G., Salmela, L., Dudley, J. M., Brunner, D., Kokhanovskiy, A. Y., Kobtsev, S. M., & Turitsyn, S. K. (2020) · Nature Photonics · doi:10.1038/s41566-020-00716-4 · Cited by 431
Performing optical logic operations by a diffractive neural network
Qian, C., Lin, X., Lin, X., Xu, J., Sun, Y., Li, E., Zhang, B., & Chen, H. (2020) · Light: Science & Applications · doi:10.1038/s41377-020-0303-2 · Cited by 413
Self-citationFourier-space Diffractive Deep Neural Network
Yan, T., Wu, J., Zhou, T., Xie, H., Xu, F., Fan, J., Fang, L., Lin, X., & Dai, Q. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.023901 · Cited by 421
Self-citationDeep Neural Network Inverse Design of Integrated Photonic Power Splitters
Tahersima, M. H., Kojima, K., Koike–Akino, T., Jha, D. K., Wang, B., Lin, C.-W., & Parsons, K. (2019) · Scientific Reports · doi:10.1038/s41598-018-37952-2 · Cited by 415
Large-Scale Optical Neural Networks Based on Photoelectric Multiplication
Hamerly, R., Bernstein, L., Sludds, A., Marin Soljacic, A., & Englund, D. R. (2019) · Physical Review X · doi:10.1103/physrevx.9.021032 · Cited by 420
Space-efficient optical computing with an integrated chip diffractive neural network
Zhu, H., Zou, J., Zhang, H., Shi, Y., Luo, S., Wang, N., Cai, H. N., Wan, L., Wang, B., Jiang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28702-0 · Cited by 383
RF and microwave photonic temporal signal processing with Kerr micro-combs
Tan, M., Xu, X., Wu, J., Morandotti, R., Mitchell, A., & Moss, D. (2020) · Advances in Physics X · doi:10.1080/23746149.2020.1838946 · Cited by 382
Wave physics as an analog recurrent neural network
Hughes, T. W., Williamson, I. A. D., Minkov, M., & Fan, S. (2019) · Science Advances · doi:10.1126/sciadv.aay6946 · Cited by 380
Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G
An, J., Xu, C., Ng, D. W. K., Alexandropoulos, G. C., Huang, C., Yuen, C., & Hanzo, L. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3288261 · Cited by 358
Intelligent metasurfaces: control, communication and computing
Li, L., Zhao, H., Li, L., Li, L., & Cui, T. J. (2022) · eLight · doi:10.1186/s43593-022-00013-3 · Cited by 359
Optical secret sharing with cascaded metasurface holography
Georgi, P., Wei, Q., Sain, B., Schlickriede, C., Wang, Y., Huang, L., & Zentgraf, T. (2021) · Science Advances · doi:10.1126/sciadv.abf9718 · Cited by 350
Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible
Luo, X., Hu, Y., Ou, X., Li, X., Lai, J., Liu, N., Cheng, X., Pan, A., & Duan, H. (2022) · Light: Science & Applications · doi:10.1038/s41377-022-00844-2 · Cited by 335
Applications of optical microcombs
Sun, Y., Wu, J., Tan, M., Xu, X., Li, Y., Morandotti, R., Mitchell, A., & Moss, D. (2022) · Advances in Optics and Photonics · doi:10.1364/aop.470264 · Cited by 355
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
Yao, K., Unni, R., & Zheng, Y. (2018) · Nanophotonics · doi:10.1515/nanoph-2018-0183 · Cited by 326
Prospects and applications of on-chip lasers
Zhou, Z., Ou, X., Fang, Y., Alkhazraji, E., Xu, R., Wan, Y., & Bowers, J. E. (2023) · eLight · doi:10.1186/s43593-022-00027-x · Cited by 340
Highlighting photonics: looking into the next decade
Chen, Z., & Segev, M. (2021) · eLight · doi:10.1186/s43593-021-00002-y · Cited by 326
Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
Xu, Z., Zhou, T., Ma, M., Deng, C., Dai, Q., & Fang, L. (2024) · Science · doi:10.1126/science.adl1203 · Cited by 341
Intelligent Computing: The Latest Advances, Challenges, and Future
Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., Gündüz, D., Hossain, E., Jin, Y., Lin, F., et al. (2023) · Intelligent Computing · doi:10.34133/icomputing.0006 · Cited by 334
Artificial neural networks enabled by nanophotonics
Zhang, Q., Yu, H., Barbiero, M., Wang, B., & Gu, M. (2019) · Light Science & Applications · doi:10.1038/s41377-019-0151-0 · Cited by 321
High-throughput terahertz imaging: progress and challenges
Li, X., Li, J., Li, Y., Özcan, A., & Jarrahi, M. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01278-0 · Cited by 313
Self-citationResponsive materials architected in space and time
Xia, X., Spadaccini, C. M., & Greer, J. R. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00450-z · Cited by 317
All-analog photoelectronic chip for high-speed vision tasks
Chen, Y., Nazhamaiti, M., Xu, H., Meng, Y., Zhou, T., Li, G., Fan, J., Wei, Q., Wu, J., Qiao, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06558-8 · Cited by 319
A silicon photonic–electronic neural network for fibre nonlinearity compensation
Huang, C., Fujisawa, S., de Lima, T. F., Tait, A. N., Blow, E. C., Tian, Y., Bilodeau, S., Jha, A., Yaman, F., Peng, H.-T., et al. (2021) · Nature Electronics · doi:10.1038/s41928-021-00661-2 · Cited by 306
Photonic machine learning with on-chip diffractive optics
Fu, T., Zang, Y., Huang, Y., Du, Z., Huang, H., Hu, C., Chen, M., Yang, S., & Chen, H. (2023) · Nature Communications · doi:10.1038/s41467-022-35772-7 · Cited by 307
Design of task-specific optical systems using broadband diffractive neural networks
Luo, Y., Mengü, D., Yardimci, N. T., Rivenson, Y., Veli, M., Jarrahi, M., & Özcan, A. (2019) · Light Science & Applications · doi:10.1038/s41377-019-0223-1 · Cited by 304
Self-citationOptical neural networks: progress and challenges
Fu, T., Zhang, J., Sun, R. C., Huang, Y., Xu, W. X., Yang, S., Zhu, Z., & Chen, H. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01590-3 · Cited by 310
Photonic multiplexing techniques for neuromorphic computing
Bai, Y., Xu, X., Tan, M., Sun, Y., Li, Y., Wu, J., Morandotti, R., Mitchell, A., Xu, K., & Moss, D. (2023) · Nanophotonics · doi:10.1515/nanoph-2022-0485 · Cited by 316
Empowering Metasurfaces with Inverse Design: Principles and Applications
Li, Z., Pestourie, R., Lin, Z., Johnson, S. G., & Capasso, F. (2022) · ACS Photonics · doi:10.1021/acsphotonics.1c01850 · Cited by 283
Multidimensional phase singularities in nanophotonics
Ni, J., Huang, C., Zhou, L., Gu, M., Song, Q., Kivshar, Y. S., & Qiu, C. (2021) · Science · doi:10.1126/science.abj0039 · Cited by 276
An optical neural network using less than 1 photon per multiplication
Wang, T., Ma, S.-Y., Wright, L. G., Onodera, T., Richard, B. C., & McMahon, P. L. (2022) · Nature Communications · doi:10.1038/s41467-021-27774-8 · Cited by 290
A guidance to intelligent metamaterials and metamaterials intelligence
Qian, C., Kaminer, I. E., & Chen, H. (2025) · Nature Communications · doi:10.1038/s41467-025-56122-3 · Cited by 282
On the use of deep learning for phase recovery
Wang, K., Song, L., Wang, C., Ren, Z., Zhao, G., Dou, J., Di, J., Barbastathis, G., Zhou, R., Zhao, J., et al. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01340-x · Cited by 257
Superhigh-Resolution Recognition of Optical Vortex Modes Assisted by a Deep-Learning Method
Liu, Z., Yan, S., Liu, H., & Chen, X. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.183902 · Cited by 258
Mengu, D., Luo, Y., Rivenson, Y., & Ozcan, A. (2020) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2921376 · Cited by 255
Self-citationAn all-optical neuron with sigmoid activation function
Mourgias-Alexandris, G., Tsakyridis, A., Passalis, N., Tefas, A., Vyrsokinos, K., & Pleros, N. (2019) · Optics Express · doi:10.1364/oe.27.009620 · Cited by 254
In situ optical backpropagation training of diffractive optical neural networks
Zhou, T., Fang, L., Yan, T., Wu, J., Li, Y., Fan, J., Wu, H., Lin, X., & Dai, Q. (2020) · Photonics Research · doi:10.1364/prj.389553 · Cited by 249
Self-citationMachine-learning-assisted metasurface design for high-efficiency thermal emitter optimization
Kudyshev, Z. A., Kildishev, A. V., Shalaev, V. M., & Boltasseva, A. (2020) · Applied Physics Reviews · doi:10.1063/1.5134792 · Cited by 240
Extreme ultra-reliable and low-latency communication
Park, J., Samarakoon, S., Shiri, H., Abdel-Aziz, M. K., Nishio, T., Elgabli, A., & Bennis, M. (2022) · Nature Electronics · doi:10.1038/s41928-022-00728-8 · Cited by 237
Neuromorphic Computing Based on Wavelength-Division Multiplexing
Xu, X., Han, W., Tan, M., Sun, Y., Li, Y., Wu, J., Morandotti, R., Mitchell, A., Xu, K., & Moss, D. (2022) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2022.3203159 · Cited by 261
Artificial Intelligence in Meta-optics
Chen, M. K., Liu, X., Sun, Y., & Tsai, D. P. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00012 · Cited by 234
A Review of Optical Neural Networks
Sui, X., Wu, Q., Liu, J., Chen, Q., & Gu, G. (2020) · IEEE Access · doi:10.1109/access.2020.2987333 · Cited by 238
Spectrally encoded single-pixel machine vision using diffractive networks
Li, J., Mengü, D., Yardimci, N. T., Luo, Y., Li, X., Veli, M., Rivenson, Y., Jarrahi, M., & Özcan, A. (2021) · Science Advances · doi:10.1126/sciadv.abd7690 · Cited by 227
Self-citationMassively parallel amplitude-only Fourier neural network
Miscuglio, M., Hu, Z., Li, S., George, J., Capanna, R., Dalir, H., Bardet, P. M., Gupta, P., & Sorger, V. J. (2020) · Optica · doi:10.1364/optica.408659 · Cited by 221
Diffractive optical computing in free space
Hu, J., Mengü, D., Tzarouchis, D. C., Edwards, B. E., Engheta, N., & Özcan, A. (2024) · Nature Communications · doi:10.1038/s41467-024-45982-w · Cited by 219
An integrated large-scale photonic accelerator with ultralow latency
Hua, S., Divita, E., Yu, S., Peng, B., Roques‐Carmes, C., Su, Z., Zhang, C., Bai, Y., Zou, J., Zhu, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08786-6 · Cited by 238
Tackling Photonic Inverse Design with Machine Learning
Liu, Z., Zhu, D., Raju, L., & Cai, W. (2021) · Advanced Science · doi:10.1002/advs.202002923 · Cited by 220
Advances in optical metalenses
Arbabi, A., & Faraon, A. (2022) · Nature Photonics · doi:10.1038/s41566-022-01108-6 · Cited by 221
Hooker, S. (2021) · Communications of the ACM · doi:10.1145/3467017 · Cited by 211
Compact optical convolution processing unit based on multimode interference
Meng, X., Zhang, G., Shi, N., Li, G., Azaña, J., Capmany, J., Yao, J., Shen, Y., Li, W., Zhu, N., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38786-x · Cited by 209
Photonic tensor cores for machine learning
Miscuglio, M., & Sorger, V. J. (2020) · Applied Physics Reviews · doi:10.1063/5.0001942 · Cited by 206
Oudich, M., Gerard, N. J., Deng, Y., & Jing, Y. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202206309 · Cited by 211
Scalable reservoir computing on coherent linear photonic processor
Nakajima, M., Tanaka, K., & Hashimoto, T. (2021) · Communications Physics · doi:10.1038/s42005-021-00519-1 · Cited by 203
Research progress in optical neural networks: theory, applications and developments
Liu, J., Wu, Q., Sui, X., Chen, Q., Gu, G., Wang, L., & Li, S. (2021) · PhotoniX · doi:10.1186/s43074-021-00026-0 · Cited by 201
Deep learning with coherent VCSEL neural networks
Chen, Z., Sludds, A., Davis, R., Christen, I., Bernstein, L., Ateshian, L., Heuser, T., Heermeier, N., Lott, J. A., Reitzenstein, S., et al. (2023) · Nature Photonics · doi:10.1038/s41566-023-01233-w · Cited by 206
Revisiting the Design Strategies for Metasurfaces: Fundamental Physics, Optimization, and Beyond
So, S., Mun, J., Park, J., & Rho, J. (2022) · Advanced Materials · doi:10.1002/adma.202206399 · Cited by 197
Diffractive Deep Neural Networks at Visible Wavelengths
Chen, H., Feng, J., Jiang, M., Wang, Y., Lin, J., Tan, J., & Jin, P. (2021) · Engineering · doi:10.1016/j.eng.2020.07.032 · Cited by 197
Prospects and applications of photonic neural networks
Huang, C., Sorger, V. J., Miscuglio, M., Al-Qadasi, M., Mukherjee, A., Lampe, L., Nichols, M., Tait, A. N., de Lima, T. F., Márquez, B. A., et al. (2021) · Advances in Physics X · doi:10.1080/23746149.2021.1981155 · Cited by 194
Terahertz pulse shaping using diffractive surfaces
Veli, M., Mengu, D., Yardimci, N. T., Luo, Y., Li, J., Rivenson, Y., Jarrahi, M., & Özcan, A. (2021) · Nature Communications · doi:10.1038/s41467-020-20268-z · Cited by 186
Self-citationUniversal photonic artificial intelligence acceleration
Ahmed, S. R., Baghdadi, R., Bernadskiy, M., Bowman, N., Braid, R., Carr, J., Chen, C., Ciccarella, P., Cole, M., Cooke, J., et al. (2025) · Nature · doi:10.1038/s41586-025-08854-x · Cited by 199
Stacked Intelligent Metasurface-Aided MIMO Transceiver Design
An, J., Yuen, C., Xu, C., Li, H., Ng, D. W. K., Renzo, M. D., Debbah, M. A., & Hanzo, L. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.013.2300259 · Cited by 181
Li, J., Mengü, D., Luo, Y., Rivenson, Y., & Özcan, A. (2019) · Advanced Photonics · doi:10.1117/1.ap.1.4.046001 · Cited by 179
Self-citationIn-memory photonic dot-product engine with electrically programmable weight banks
Zhou, W. Y., Dong, B., Farmakidis, N., Li, X., Youngblood, N., Huang, K., He, Y., Wright, C. D., Pernice, W. H. P., & Bhaskaran, H. (2023) · Nature Communications · doi:10.1038/s41467-023-38473-x · Cited by 175
Jin, Y., Pennec, Y., Bonello, B., Honarvar, H., Dobrzyński, L., Djafari‐Rouhani, B., & Hussein, M. I. (2021) · Reports on Progress in Physics · doi:10.1088/1361-6633/abdab8 · Cited by 180
Computational imaging without a computer: seeing through random diffusers at the speed of light
Luo, Y., Zhao, Y., Li, J., Çetintaş, E., Rivenson, Y., Jarrahi, M., & Özcan, A. (2022) · eLight · doi:10.1186/s43593-022-00012-4 · Cited by 173
Self-citationGoi, E., Chen, X., Zhang, Q., Cumming, B. P., Schoenhardt, S., Luan, H., & Gu, M. (2021) · Light Science & Applications · doi:10.1038/s41377-021-00483-z · Cited by 173
Marcucci, G., Pierangeli, D., & Conti, C. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.093901 · Cited by 178
Intelligent metaphotonics empowered by machine learning
Krasikov, S., Tranter, A. D., Bogdanov, A., & Kivshar, Y. S. (2022) · Opto-Electronic Advances · doi:10.29026/oea.2022.210147 · Cited by 169
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
