*Result*: Dispersion-Engineered Terahertz Spoof Plasmonic Neural Network for Parallel Computing and On-Chip Communication.
Title:
Dispersion-Engineered Terahertz Spoof Plasmonic Neural Network for Parallel Computing and On-Chip Communication.
Authors:
Gao X; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong SAR, China.; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Ma Q; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Gu Z; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Shum KM; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong SAR, China., Chen BJ; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong SAR, China., Li RS; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Cui WY; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Cui TJ; State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China., Chan CH; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong SAR, China.
Source:
Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2026 Feb; Vol. 38 (10), pp. e03584. Date of Electronic Publication: 2025 Dec 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 9885358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1521-4095 (Electronic) Linking ISSN: 09359648 NLM ISO Abbreviation: Adv Mater Subsets: MEDLINE; PubMed not MEDLINE
Imprint Name(s):
Publication: Sept. 3, 1997- : Weinheim : Wiley-VCH
Original Publication: Deerfield Beach, FL : VCH Publishers, 1989-
Original Publication: Deerfield Beach, FL : VCH Publishers, 1989-
References:
G. Hinton, L. Deng, D. Yu, et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine 29 (2012): 82–97, https://doi.org/10.1109/MSP.2012.2205597.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature 521 (2015): 436–444, https://doi.org/10.1038/nature14539.
I. S. Alex Krizhevsky and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM 60 (2017): 84–90, https://doi.org/10.1145/3065386.
M. Moor, O. Banerjee, Z. S. H. Abad, et al., “Foundation Models for Generalist Medical Artificial Intelligence,” Nature 616 (2023): 259–265, https://doi.org/10.1038/s41586‐023‐05881‐4.
F. Ashtiani, A. J. Geers, and F. Aflatouni, “An On‐Chip Photonic Deep Neural Network for Image Classification,” Nature 606 (2022): 501–506, https://doi.org/10.1038/s41586‐022‐04714‐0.
G. Wetzstein, A. Ozcan, S. Gigan, et al., “Inference in Artificial Intelligence With Deep Optics and Photonics,” Nature 588 (2020): 39–47, https://doi.org/10.1038/s41586‐020‐2973‐6.
X. Xu, M. Tan, B. Corcoran, et al., “11 TOPS Photonic Convolutional Accelerator for Optical Neural Networks,” Nature 589 (2021): 44–51, https://doi.org/10.1038/s41586‐020‐03063‐0.
H. Wu and Q. Dai, “Artificial Intelligence Accelerated by Light,” Nature 589 (2021): 25–26.
A. Najjar Amiri, A. D. Vit, K. Gorgulu, and E. S. Magden, “Deep Photonic Network Platform Enabling Arbitrary and Broadband Optical Functionality,” Nature Communications 15 (2024): 1432, https://doi.org/10.1038/s41467‐024‐45846‐3.
H. Zhang, M. Gu, X. D. Jiang, et al., “An Optical Neural Chip for Implementing Complex‐Valued Neural Network,” Nature Communications 12 (2021): 457, https://doi.org/10.1038/s41467‐020‐20719‐7.
Z. Du, K. Liao, T. Dai, et al., “Ultracompact and Multifunctional Integrated Photonic Platform,” Science Advances 10 (2024): adm7569, https://doi.org/10.1126/sciadv.adm7569.
J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. P. Pernice, “All‐Optical Spiking Neurosynaptic Networks With Self‐Learning Capabilities,” Nature 569 (2019): 208–214, https://doi.org/10.1038/s41586‐019‐1157‐8.
X. Lin, Y. Rivenson, N. T. Yardimci, et al., “All‐Optical Machine Learning Using Diffractive Deep Neural Networks,” Science 361 (2018): 1004–1008.
C. Liu, Q. Ma, Z. J. Luo, et al., “A Programmable Diffractive Deep Neural Network Based on a Digital‐Coding Metasurface Array,” Nature Electronics 5 (2022): 113–122, https://doi.org/10.1038/s41928‐022‐00719‐9.
C. Qian, X. Lin, X. Lin, et al., “Performing Optical Logic Operations by a Diffractive Neural Network,” Light: Science & Applications 9 (2020): 59.
T. Wang, M. M. Sohoni, L. G. Wright, et al., “Image Sensing with Multilayer Nonlinear Optical Neural Networks,” Nature Photonics 17 (2023): 408–415, https://doi.org/10.1038/s41566‐023‐01170‐8.
S. Gao, H. Chen, Y. Wang, et al., “Super‐Resolution Diffractive Neural Network for All‐Optical Direction of Arrival Estimation Beyond Diffraction Limits,” Light: Science & Applications 13 (2024): 161, https://doi.org/10.1038/s41377‐024‐01511‐4.
J. Hu, D. Mengu, D. C. Tzarouchis, B. Edwards, N. Engheta, and A. Ozcan, “Diffractive Optical Computing in Free Space,” Nature Communications 15 (2024): 1525, https://doi.org/10.1038/s41467‐024‐45982‐w.
T. Fu, Y. Zang, Y. Huang, et al., “Photonic Machine Learning with On‐Chip Diffractive Optics,” Nature Communications 14 (2023): 70, https://doi.org/10.1038/s41467‐022‐35772‐7.
Z. Wang, L. Chang, F. Wang, T. Li, and T. Gu, “Integrated Photonic Metasystem for Image Classifications at Telecommunication Wavelength,” Nature Communications 13 (2022): 2131, https://doi.org/10.1038/s41467‐022‐29856‐7.
J. Cheng, C. Huang, J. Zhang, et al., “Multimodal Deep Learning Using On‐Chip Diffractive Optics With In Situ Training Capability,” Nature Communications 15 (2024): 6189, https://doi.org/10.1038/s41467‐024‐50677‐3.
X. Gao, Z. Gu, Q. Ma, et al., “Terahertz Spoof Plasmonic Neural Network for Diffractive Information Recognition and Processing,” Nature Communications 15 (2024): 6686.
Z. Gu, Q. Ma, X. Gao, J. W. You, and T. J. Cui, “Direct Electromagnetic Information Processing With Planar Diffractive Neural Network,” Science Advances 10 (2024): ado3937, https://doi.org/10.1126/sciadv.ado3937.
X. Gao, Q. Ma, Z. Gu, et al., “Programmable Surface Plasmonic Neural Networks for Microwave Detection and Processing,” Nature Electronics 6 (2023): 319–328, https://doi.org/10.1038/s41928‐023‐00951‐x.
Y. Chen, M. Nazhamaiti, H. Xu, et al., “All‐Analog Photoelectronic Chip for High‐Speed Vision Tasks,” Nature 623 (2023): 48–57, https://doi.org/10.1038/s41586‐023‐06558‐8.
W. T. Chen, A. Y. Zhu, and F. Capasso, “Flat Optics With Dispersion‐Engineered Metasurfaces,” Nature Reviews Materials 5 (2020): 604–620, https://doi.org/10.1038/s41578‐020‐0203‐3.
X. Hua, Y. Wang, S. Wang, et al., “Ultra‐Compact Snapshot Spectral Light‐Field Imaging,” Nature Communications 13 (2022): 2732, https://doi.org/10.1038/s41467‐022‐30439‐9.
Z. B. Fan, H. Y. Qiu, H. L. Zhang, et al., “A Broadband Achromatic Metalens Array for Integral Imaging in the Visible,” Light: Science & Applications 8 (2019): 67, https://doi.org/10.1038/s41377‐019‐0178‐2.
Y. Luo, D. Mengu, N. T. Yardimci, et al., “Design of Task‐Specific Optical Systems Using Broadband Diffractive Neural Networks,” Light: Science & Applications 8 (2019): 112, https://doi.org/10.1038/s41377‐019‐0223‐1.
Z. Duan, H. Chen, and X. Lin, “Optical Multi‐Task Learning Using Multi‐Wavelength Diffractive Deep Neural Networks,” Nanophotonics 12 (2023): 893–903, https://doi.org/10.1515/nanoph‐2022‐0615.
X. Gao, H. C. Zhang, P. H. He, et al., “Crosstalk Suppression Based on Mode Mismatch Between Spoof SPP Transmission Line and Microstrip,” IEEE Transactions on Components, Packaging and Manufacturing Technology 9 (2019): 2267–2275, https://doi.org/10.1109/TCPMT.2019.2931373.
H. C. Zhang, L. P. Zhang, P. H. He, et al., “A Plasmonic Route for the Integrated Wireless Communication of Subdiffraction‐Limited Signals,” Light: Science & Applications 9 (2020): 113, https://doi.org/10.1038/s41377‐020‐00355‐y.
X. Gao, W. Y. Cui, Z. Gu, et al., “Multimode and Reconfigurable Phase Shifter of Spoof Surface Plasmons,” IEEE Transactions on Antennas and Propagation 71 (2023): 5361–5369, https://doi.org/10.1109/TAP.2023.3262348.
X. Gao, H. C. Zhang, L. W. Wu, et al., “Programmable Multifunctional Device Based on Spoof Surface Plasmon Polaritons,” IEEE Transactions on Antennas and Propagation 68 (2020): 3770–3779, https://doi.org/10.1109/TAP.2020.2969745.
Q. Ma, Z. Gu, X. Gao, J. W. You, and T. J. Cui, “Terahertz Spoof Plasmonic Neural Network for Diffractive Information Recognition and Processing,” Science Advances 10 (2024): ado3937.
Z. Xue, T. Zhou, Z. Xu, S. Yu, Q. Dai, and L. Fang, “Fully Forward Mode Training for Optical Neural Networks,” Nature 632 (2024): 280–286, https://doi.org/10.1038/s41586‐024‐07687‐4.
R. T. Ako, A. Upadhyay, W. Withayachumnankul, M. Bhaskaran, and S. Sriram, “Dielectrics for Terahertz Metasurfaces: Material Selection and Fabrication Techniques,” Advanced Optical Materials 8 (2019): 1900750.
C. C. Wanjura and F. Marquardt, “Fully Nonlinear Neuromorphic Computing with Linear Wave Scattering,” Nature Physics 20 (2024): 1434–1440, https://doi.org/10.1038/s41567‐024‐02534‐9.
X. Gao, Z. Gu, Q. Ma, W. Y. Cui, T. J. Cui, and C. H. Chan, “Reprogrammable Spoof Plasmonic Modulator,” Advanced Functional Materials 33 (2023): 2212328, https://doi.org/10.1002/adfm.202212328.
S. Pai, Z. Sun, T. W. Hughes, et al., “Experimentally Realized in Situ Backpropagation for Deep Learning in Photonic Neural Networks,” Science 380 (2023): 398–404.
Y. Shen, N. C. Harris, S. Skirlo, et al., “Deep Learning with Coherent Nanophotonic Circuits,” Nature Photonics 11 (2017): 441–446, https://doi.org/10.1038/nphoton.2017.93.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature 521 (2015): 436–444, https://doi.org/10.1038/nature14539.
I. S. Alex Krizhevsky and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM 60 (2017): 84–90, https://doi.org/10.1145/3065386.
M. Moor, O. Banerjee, Z. S. H. Abad, et al., “Foundation Models for Generalist Medical Artificial Intelligence,” Nature 616 (2023): 259–265, https://doi.org/10.1038/s41586‐023‐05881‐4.
F. Ashtiani, A. J. Geers, and F. Aflatouni, “An On‐Chip Photonic Deep Neural Network for Image Classification,” Nature 606 (2022): 501–506, https://doi.org/10.1038/s41586‐022‐04714‐0.
G. Wetzstein, A. Ozcan, S. Gigan, et al., “Inference in Artificial Intelligence With Deep Optics and Photonics,” Nature 588 (2020): 39–47, https://doi.org/10.1038/s41586‐020‐2973‐6.
X. Xu, M. Tan, B. Corcoran, et al., “11 TOPS Photonic Convolutional Accelerator for Optical Neural Networks,” Nature 589 (2021): 44–51, https://doi.org/10.1038/s41586‐020‐03063‐0.
H. Wu and Q. Dai, “Artificial Intelligence Accelerated by Light,” Nature 589 (2021): 25–26.
A. Najjar Amiri, A. D. Vit, K. Gorgulu, and E. S. Magden, “Deep Photonic Network Platform Enabling Arbitrary and Broadband Optical Functionality,” Nature Communications 15 (2024): 1432, https://doi.org/10.1038/s41467‐024‐45846‐3.
H. Zhang, M. Gu, X. D. Jiang, et al., “An Optical Neural Chip for Implementing Complex‐Valued Neural Network,” Nature Communications 12 (2021): 457, https://doi.org/10.1038/s41467‐020‐20719‐7.
Z. Du, K. Liao, T. Dai, et al., “Ultracompact and Multifunctional Integrated Photonic Platform,” Science Advances 10 (2024): adm7569, https://doi.org/10.1126/sciadv.adm7569.
J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. P. Pernice, “All‐Optical Spiking Neurosynaptic Networks With Self‐Learning Capabilities,” Nature 569 (2019): 208–214, https://doi.org/10.1038/s41586‐019‐1157‐8.
X. Lin, Y. Rivenson, N. T. Yardimci, et al., “All‐Optical Machine Learning Using Diffractive Deep Neural Networks,” Science 361 (2018): 1004–1008.
C. Liu, Q. Ma, Z. J. Luo, et al., “A Programmable Diffractive Deep Neural Network Based on a Digital‐Coding Metasurface Array,” Nature Electronics 5 (2022): 113–122, https://doi.org/10.1038/s41928‐022‐00719‐9.
C. Qian, X. Lin, X. Lin, et al., “Performing Optical Logic Operations by a Diffractive Neural Network,” Light: Science & Applications 9 (2020): 59.
T. Wang, M. M. Sohoni, L. G. Wright, et al., “Image Sensing with Multilayer Nonlinear Optical Neural Networks,” Nature Photonics 17 (2023): 408–415, https://doi.org/10.1038/s41566‐023‐01170‐8.
S. Gao, H. Chen, Y. Wang, et al., “Super‐Resolution Diffractive Neural Network for All‐Optical Direction of Arrival Estimation Beyond Diffraction Limits,” Light: Science & Applications 13 (2024): 161, https://doi.org/10.1038/s41377‐024‐01511‐4.
J. Hu, D. Mengu, D. C. Tzarouchis, B. Edwards, N. Engheta, and A. Ozcan, “Diffractive Optical Computing in Free Space,” Nature Communications 15 (2024): 1525, https://doi.org/10.1038/s41467‐024‐45982‐w.
T. Fu, Y. Zang, Y. Huang, et al., “Photonic Machine Learning with On‐Chip Diffractive Optics,” Nature Communications 14 (2023): 70, https://doi.org/10.1038/s41467‐022‐35772‐7.
Z. Wang, L. Chang, F. Wang, T. Li, and T. Gu, “Integrated Photonic Metasystem for Image Classifications at Telecommunication Wavelength,” Nature Communications 13 (2022): 2131, https://doi.org/10.1038/s41467‐022‐29856‐7.
J. Cheng, C. Huang, J. Zhang, et al., “Multimodal Deep Learning Using On‐Chip Diffractive Optics With In Situ Training Capability,” Nature Communications 15 (2024): 6189, https://doi.org/10.1038/s41467‐024‐50677‐3.
X. Gao, Z. Gu, Q. Ma, et al., “Terahertz Spoof Plasmonic Neural Network for Diffractive Information Recognition and Processing,” Nature Communications 15 (2024): 6686.
Z. Gu, Q. Ma, X. Gao, J. W. You, and T. J. Cui, “Direct Electromagnetic Information Processing With Planar Diffractive Neural Network,” Science Advances 10 (2024): ado3937, https://doi.org/10.1126/sciadv.ado3937.
X. Gao, Q. Ma, Z. Gu, et al., “Programmable Surface Plasmonic Neural Networks for Microwave Detection and Processing,” Nature Electronics 6 (2023): 319–328, https://doi.org/10.1038/s41928‐023‐00951‐x.
Y. Chen, M. Nazhamaiti, H. Xu, et al., “All‐Analog Photoelectronic Chip for High‐Speed Vision Tasks,” Nature 623 (2023): 48–57, https://doi.org/10.1038/s41586‐023‐06558‐8.
W. T. Chen, A. Y. Zhu, and F. Capasso, “Flat Optics With Dispersion‐Engineered Metasurfaces,” Nature Reviews Materials 5 (2020): 604–620, https://doi.org/10.1038/s41578‐020‐0203‐3.
X. Hua, Y. Wang, S. Wang, et al., “Ultra‐Compact Snapshot Spectral Light‐Field Imaging,” Nature Communications 13 (2022): 2732, https://doi.org/10.1038/s41467‐022‐30439‐9.
Z. B. Fan, H. Y. Qiu, H. L. Zhang, et al., “A Broadband Achromatic Metalens Array for Integral Imaging in the Visible,” Light: Science & Applications 8 (2019): 67, https://doi.org/10.1038/s41377‐019‐0178‐2.
Y. Luo, D. Mengu, N. T. Yardimci, et al., “Design of Task‐Specific Optical Systems Using Broadband Diffractive Neural Networks,” Light: Science & Applications 8 (2019): 112, https://doi.org/10.1038/s41377‐019‐0223‐1.
Z. Duan, H. Chen, and X. Lin, “Optical Multi‐Task Learning Using Multi‐Wavelength Diffractive Deep Neural Networks,” Nanophotonics 12 (2023): 893–903, https://doi.org/10.1515/nanoph‐2022‐0615.
X. Gao, H. C. Zhang, P. H. He, et al., “Crosstalk Suppression Based on Mode Mismatch Between Spoof SPP Transmission Line and Microstrip,” IEEE Transactions on Components, Packaging and Manufacturing Technology 9 (2019): 2267–2275, https://doi.org/10.1109/TCPMT.2019.2931373.
H. C. Zhang, L. P. Zhang, P. H. He, et al., “A Plasmonic Route for the Integrated Wireless Communication of Subdiffraction‐Limited Signals,” Light: Science & Applications 9 (2020): 113, https://doi.org/10.1038/s41377‐020‐00355‐y.
X. Gao, W. Y. Cui, Z. Gu, et al., “Multimode and Reconfigurable Phase Shifter of Spoof Surface Plasmons,” IEEE Transactions on Antennas and Propagation 71 (2023): 5361–5369, https://doi.org/10.1109/TAP.2023.3262348.
X. Gao, H. C. Zhang, L. W. Wu, et al., “Programmable Multifunctional Device Based on Spoof Surface Plasmon Polaritons,” IEEE Transactions on Antennas and Propagation 68 (2020): 3770–3779, https://doi.org/10.1109/TAP.2020.2969745.
Q. Ma, Z. Gu, X. Gao, J. W. You, and T. J. Cui, “Terahertz Spoof Plasmonic Neural Network for Diffractive Information Recognition and Processing,” Science Advances 10 (2024): ado3937.
Z. Xue, T. Zhou, Z. Xu, S. Yu, Q. Dai, and L. Fang, “Fully Forward Mode Training for Optical Neural Networks,” Nature 632 (2024): 280–286, https://doi.org/10.1038/s41586‐024‐07687‐4.
R. T. Ako, A. Upadhyay, W. Withayachumnankul, M. Bhaskaran, and S. Sriram, “Dielectrics for Terahertz Metasurfaces: Material Selection and Fabrication Techniques,” Advanced Optical Materials 8 (2019): 1900750.
C. C. Wanjura and F. Marquardt, “Fully Nonlinear Neuromorphic Computing with Linear Wave Scattering,” Nature Physics 20 (2024): 1434–1440, https://doi.org/10.1038/s41567‐024‐02534‐9.
X. Gao, Z. Gu, Q. Ma, W. Y. Cui, T. J. Cui, and C. H. Chan, “Reprogrammable Spoof Plasmonic Modulator,” Advanced Functional Materials 33 (2023): 2212328, https://doi.org/10.1002/adfm.202212328.
S. Pai, Z. Sun, T. W. Hughes, et al., “Experimentally Realized in Situ Backpropagation for Deep Learning in Photonic Neural Networks,” Science 380 (2023): 398–404.
Y. Shen, N. C. Harris, S. Skirlo, et al., “Deep Learning with Coherent Nanophotonic Circuits,” Nature Photonics 11 (2017): 441–446, https://doi.org/10.1038/nphoton.2017.93.
Grant Information:
AoE/E-101/23-N University Grants Committee/Research Grants Council of the Hong Kong Special Administrative Region, China; BK20212002 Major Project of Natural Science Foundation of Jiangsu Province; BK20210209 Major Project of Natural Science Foundation of Jiangsu Province; 2242023K5002 Fundamental Research Funds for the Central Universities
Contributed Indexing:
Keywords: diffractive neural network; spoof plasmonic metamaterials; terahertz on‐chip communication
Entry Date(s):
Date Created: 20251226 Latest Revision: 20260217
Update Code:
20260218
DOI:
10.1002/adma.202503584
PMID:
41452160
Database:
MEDLINE
*Further Information*
*Diffractive neural networks offer a novel physical implementation for optical computing to achieve parallelism, low power consumption, and light-speed processing. However, their limited dispersion engineering necessitates increasingly complex architectures for tasks such as spectrum recognition and simultaneous multi-class classification, which in turn leads to increased energy demands. Here, we propose a spoof plasmonic neural network (SPNN) comprising cross-cascaded spoof surface plasmonic waveguides with strong engineered dispersion properties designed for operation in the terahertz regime. This compact platform efficiently separates spectral components from a broadband input signal, achieving a data rate of 22 Gbit/s across two separated spectral channels. We experimentally show that the SPNN can simultaneously classify multiple inputs from Fashion-MNIST+MNIST or Fashion-MNIST+EMNIST datasets, achieving classification accuracies of 98.3% and 97.4% or 97.4% and 93.8%, respectively. For multi-color CIFAR-10 dataset classification, the network architecture incorporating multiple cascaded SPNNs realizes over 10% higher accuracy than single-color-channel methods by leveraging distinct color channels mapped to respective spectrum channels. These findings highlight the potential of SPNNs for machine learning applications and lay the groundwork for future terahertz chip integration.
(© 2025 Wiley‐VCH GmbH.)*