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A programmable diffractive deep neural network based on a digital-coding metasurface array
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“Since the matrix operations in (2) occur automatically during the wave propagation through free space, SIMs can execute complex signal processing and computing tasks without requiring any digital storage, transmission, or preprocessing of information [24].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [24]
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“Furthermore, Liu et al. [24] developed a reconfigurable SIM using programmable metasurfaces that can dynamically adjust its EM behavior in real time. Specifically, each metaatom (neuron) is connected to two amplifier chips, providing a dynamic modulation range of 35 dB. A five-layer SIM prototype was fabricated to demonstrate its capability for image recognition and multi-beam focusing [24].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [24]
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“The electronically programmable metaatoms within a SIM function similarly to artificial neurons in an ANN, with their complex-valued transmission coefficients serving as trainable network parameters [24].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [24]
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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 [24]
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Broadband multifunctional metasurface for dynamic wavefront modulation
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Ultra-robust informational metasurfaces based on spatial coherence structures engineering
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Rong, C., Wu, L., Tao, J., Cheng, Y., Wang, K., Chen, L., Luo, H., Chen, F., & Li, X. (2025) · Journal of Lightwave Technology · doi:10.1109/jlt.2025.3592022 · Cited by 50
Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future
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Qu, K., Chen, K., Hu, Q., Zhao, J., Jiang, T., & Feng, Y. (2023) · Advanced Photonics Nexus · doi:10.1117/1.apn.2.1.016010 · Cited by 44
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Self-citationPerforming calculus with epsilon-near-zero metamaterials
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On the Efficient Design of Stacked Intelligent Metasurfaces for Secure SISO Transmission
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Self-citationTerahertz spoof plasmonic neural network for diffractive information recognition and processing
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Symbiotic evolution of photonics and artificial intelligence: a comprehensive review
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High‐Resolution Programmable Metasurface Imager Based on Multilayer Perceptron Network
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Review of diffractive deep neural networks
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Metadata
- Authors
- Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., & Cui, T. J.
- Affiliations
- State Key Laboratory of Millimeter Waves (CN)
- Peking University (CN)
- Corresponding author
- Cui, Tie Jun
- ORCID
- Venue
- Nature Electronics
- Published
- 2022-02-21
- Volume
- 5
- Issue
- 2
- Pages
- 113-122
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W4212835306
- Cited by
- 633
- References
- 45
- Citations by year
- 2026: 111 · 2025: 178 · 2024: 171 · 2023: 115 · 2022: 55
- FWCI
- 29.92
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Electronics (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- Funders
- National Key Research and Development Program of China (2017YFA0700201, 2017YFA0700203, 2017YFA0700202)
- Topics
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Metamaterials and Metasurfaces Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Antenna and Metasurface Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- diffractive deep neural networks, metasurface arrays, digital encoding, on-chip learning, reinforcement learning, discrete optimization, image classification, all-optical computing
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