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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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Research works printed but not linked: 2
- Cui, T. J., Liu, S., Bai, G. D. & Ma, Q. Direct transmission of digital message via programmable coding metasurface. Research 2019, 2584509 (2019).
- Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. in 3rd International Conference on Learning Representations (2015).
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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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