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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]
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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.
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“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]
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“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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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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