A programmable diffractive deep neural network based on a digital-coding metasurface array
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Since the matrix operations occur automatically during wave propagation through free space, SIMs can execute complex signal processing and computing tasks without requiring digital storage, transmission, or preprocessing of information.
“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]
The Main Text describes waves propagating between metasurface layers and combining at each meta-atom, supporting the claim that wave propagation performs part of the computation. But it also says the first layer converts given data into an electromagnetic-wave pattern, while the Additional information describes FPGA processors performing calculations and distributing voltage settings. The source therefore does not support the blanket claim that these tasks require no digital preprocessing, storage, or transmission of information. The indexed passages provide section labels but no page numbers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLiu et al. developed a reconfigurable SIM using programmable metasurfaces that can dynamically adjust its electromagnetic behavior in real time, with each meta-atom connected to two amplifier chips providing a dynamic modulation range of 35 dB, and a five-layer prototype demonstrated image recognition and multi-beam focusing.
“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]
The electronically programmable meta-atoms within a SIM function similarly to artificial neurons in an ANN, with their complex-valued transmission coefficients serving as trainable network parameters.
“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]
The architecture of a SIM forms an electromagnetic neural network that is conceptually similar to an artificial neural network.
“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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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 · see citing works
Information metasurfaces and intelligent metasurfaces
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Self-citationStacked 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 · see citing works
An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
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Chen, X. Q., Zhang, L., Liu, S., & Cui, T. J. (2022) · Advanced Optical Materials · doi:10.1002/adom.202201900 · Cited by 75 · see citing works
Self-citationOrbital 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 76 · see citing works
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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 · see citing works
“Restrictions apply.”
InfluentialSelf-citationPapazafeiropoulos, A., An, J., Kourtessis, P., Ratnarajah, T., & Chatzinotas, S. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3399318 · Cited by 71 · see citing works
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 71 · see citing works
Self-citationNigar, A., Li, Y., Baloch, M. Y. J., Alrefaei, A. F., & Almutairi, M. H. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1378443 · Cited by 71 · see citing works
Touch‐Programmable Metasurface for Various Electromagnetic Manipulations and Encryptions
Chen, L., Ma, Q., Luo, S., Ye, F. J., Cui, H. Y., & Cui, T. J. (2022) · Small · doi:10.1002/smll.202203871 · Cited by 70 · see citing works
Self-citationSimultaneously Intelligent Sensing and Beamforming Based on an Adaptive Information Metasurface
Jiang, R. Z., Ma, Q., Gu, Z., Liang, J. C., Xiao, Q., Cheng, Q., & Cui, T. J. (2023) · Advanced Science · doi:10.1002/advs.202306181 · Cited by 70 · see citing works
Self-citationStacked Intelligent Metasurfaces for Wireless Communications: Applications and Challenges
Liu, H., An, J., Jia, X., Gan, L., Karagiannidis, G. K., Clerckx, B., Bennis, M., Debbah, M. A., & Cui, T. J. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.001.2500002 · Cited by 68 · see citing works
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 66 · see citing works
Flexible Intelligent Metasurfaces for Downlink Multiuser MISO Communications
An, J., Yuen, C., Renzo, M. D., Debbah, M., Poor, H. V., & Hanzo, L. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3526843 · Cited by 64 · see citing works
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Joint AP-UE Association and Precoding for SIM-Aided Cell-Free Massive MIMO Systems
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Nonlinear nonlocal metasurfaces
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Channel Estimation for Stacked Intelligent Metasurface-Assisted Wireless Networks
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All‐Dielectric Metasurface Empowered Optical‐Electronic Hybrid Neural Networks
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Computational optical imaging: on the convergence of physical and digital layers
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Review for wireless communication technology based on digital encoding metasurfaces
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Design Strategies and Applications of Dimensional Optical Field Manipulation Based on Metasurfaces
Liu, W., Li, Z., Ansari, M. A., Cheng, H., Tian, J., Chen, X., & Chen, S. (2023) · Advanced Materials · doi:10.1002/adma.202208884 · Cited by 58 · see citing works
Data‐Class‐Specific All‐Optical Transformations and Encryption
Bai, B., Wei, H., Yang, X., Gan, T., Mengü, D., Jarrahi, M., & Özcan, A. (2023) · Advanced Materials · doi:10.1002/adma.202212091 · Cited by 57 · see citing works
Machine learning in design of broadband terahertz absorbers based on composite structures
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Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning
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Experimental Demonstration of Controllable PT and Anti- PT Coupling in a Non-Hermitian Metamaterial
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Efficient Beamforming and Radiation Pattern Control Using Stacked Intelligent Metasurfaces
Hassan, N. U., An, J., Di Renzo, M., Debbah, M., & Yuen, C. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3349155 · Cited by 55 · see citing works
Meta‐Attention Deep Learning for Smart Development of Metasurface Sensors
Gao, Y., Chen, W., Li, F., Zhuang, M., Yan, Y., Wang, J., Wang, X., Dong, Z., Ma, W., & Zhu, J. (2024) · Advanced Science · doi:10.1002/advs.202405750 · Cited by 55 · see citing works
Flexible intelligent microwave metasurface with shape-guided adaptive programming
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Intelligent electromagnetic metasurface camera: system design and experimental results
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Physics‐Informed Inverse Design of Programmable Metasurfaces
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Optical computing metasurfaces: applications and advances
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Tailless Information–Energy Metasurface
Chang, M., Mu, Y., Han, J., Li, G., Li, Y., Liu, H., Li, L., & Cui, T. J. (2024) · Advanced Materials · doi:10.1002/adma.202313697 · Cited by 49 · see citing works
Self-citationDirect electromagnetic information processing with planar diffractive neural network
Gu, Z., Ma, Q., Gao, X., You, J. W., & Cui, T. J. (2024) · Science Advances · doi:10.1126/sciadv.ado3937 · Cited by 49 · see citing works
Self-citationCompact User-Specific Reconfigurable Intelligent Surfaces for Uplink Transmission
Liu, K., Zhang, Z., Dai, L., & Hanzo, L. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3124953 · Cited by 49 · see citing works
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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Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future
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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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- 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 · 2021: 3
- 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 › Aerospace 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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