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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
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“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.
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“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]
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“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]
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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
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Ju, Y. (2023) · Journal of Imaging · doi:10.3390/jimaging9110241 · Cited by 12
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Debrunner, T., Sajad Saeedi, G., & Kelly, P. H. J. (2018) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3291055 · Cited by 12
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Yung, T. K., Liang, H., Xi, J., Tam, W. Y., & Li, J. (2022) · Nanophotonics · doi:10.1515/nanoph-2022-0499 · Cited by 12
Xiong, J., Cheng, J., Deng, H., Hua, Y., Zhang, Y., Du, Z., Zhao, L., Deng, N., Li, W., Zhang, Z., et al. (2023) · IEEE photonics journal · doi:10.1109/jphot.2023.3273772 · Cited by 12
Liu, X., Chen, M. K., & Tsai, D. P. (2023) · Laser & Photonics Review · doi:10.1002/lpor.202300456 · Cited by 13
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Isomae, Y., Sugawara, N., Iwasaki, N., Honda, T., & Amari, K. (2021) · Digital Optical Technologies · doi:10.1117/12.2595136 · Cited by 12
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Moss, D. (2022) · Preprints.org · doi:10.20944/preprints202206.0179.v1 · Cited by 12
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Yu, L., Singh, H. J., Pietila, J., & Çağlayan, H. (2026) · Light Science & Applications · doi:10.1038/s41377-025-02153-w · Cited by 13
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Wang, Y., Guan, C., Li, H., Ding, X., Zhang, K., Wang, J., Burokur, S. N., Liu, J., & Wu, Q. (2020) · Advanced Photonics Research · doi:10.1002/adpr.202000022 · Cited by 11
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Lugnan, A., Gooskens, E., Vatin, J., Dambre, J., & Bienstman, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-77765-w · Cited by 11
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Chen, J., Peng, J., Yang, C., Dun, X., Cheng, X., Chen, H., & Ren, J. (2021) · Applied Physics Letters · doi:10.1063/5.0059010 · Cited by 11
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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
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