• Acta Optica Sinica
  • Vol. 41, Issue 8, 0823004 (2021)
Che Liu1、2, Qian Ma1、2, Lianlin Li3, and Tiejun Cui1、2、*
Author Affiliations
  • 1Institute of Electromagnetic Space, Southeast University, Nanjing, Jiangsu 210096, China
  • 2State Key Laboratory of Millimeter Wave, Southeast University, Nanjing, Jiangsu 210096, China
  • 3State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, Beijing 100871, China
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    DOI: 10.3788/AOS202141.0823004 Cite this Article Set citation alerts
    Che Liu, Qian Ma, Lianlin Li, Tiejun Cui. Artificial Intelligence Metamaterials[J]. Acta Optica Sinica, 2021, 41(8): 0823004 Copy Citation Text show less
    Geometric structure of coding unit cell[78]. (a) Top view; (b) cross-sectional view
    Fig. 1. Geometric structure of coding unit cell[78]. (a) Top view; (b) cross-sectional view
    Flow chart of joint optimization by BPSO algorithm together with CST simulation software[78]
    Fig. 2. Flow chart of joint optimization by BPSO algorithm together with CST simulation software[78]
    Models, reflection phases and amplitudes of 1 bit coding unit cells[78]. (a) Geometric diagram of first coding unit cell; (b) geometric diagram of second coding unit cell; (c) reflection phases of 1 bit coding unit cells and their phase difference; (d) reflection amplitudes of 1 bit coding unit cells
    Fig. 3. Models, reflection phases and amplitudes of 1 bit coding unit cells[78]. (a) Geometric diagram of first coding unit cell; (b) geometric diagram of second coding unit cell; (c) reflection phases of 1 bit coding unit cells and their phase difference; (d) reflection amplitudes of 1 bit coding unit cells
    Structural diagram of coding unit cell[82]
    Fig. 4. Structural diagram of coding unit cell[82]
    Flow chart of machine learning design for 1 bit coding unit with phase difference in orthogonal directions[82]
    Fig. 5. Flow chart of machine learning design for 1 bit coding unit with phase difference in orthogonal directions[82]
    Comparison of non-iterative deep learning method and conventional optimization method[84]
    Fig. 6. Comparison of non-iterative deep learning method and conventional optimization method[84]
    Schematic of layered optical metamaterial[85]. (a) Thin films composed of multiple layers of SiO2 and Si3N4; (b)(c) two 6-layered thin film structures with similar transmission spectra and their corresponding spectra
    Fig. 7. Schematic of layered optical metamaterial[85]. (a) Thin films composed of multiple layers of SiO2 and Si3N4; (b)(c) two 6-layered thin film structures with similar transmission spectra and their corresponding spectra
    Tandem network composed of inverse design network and forward modeling network[85]
    Fig. 8. Tandem network composed of inverse design network and forward modeling network[85]
    Schematic of GAN-based inverse design method[86]
    Fig. 9. Schematic of GAN-based inverse design method[86]
    All-optical diffractive neural networks[90]. (a) Schematic of network structure; (b) schematic of 3D printing structure
    Fig. 10. All-optical diffractive neural networks[90]. (a) Schematic of network structure; (b) schematic of 3D printing structure
    Imaging principle and imaging results[77]. (a) Principle diagram of imaging algorithm; (b) four images of testing person; (c) reconstructed images corresponding to Fig.11(b) using principal component analysis method; (d) reconstructed images corresponding to Fig.11(b) using random radiation pattern
    Fig. 11. Imaging principle and imaging results[77]. (a) Principle diagram of imaging algorithm; (b) four images of testing person; (c) reconstructed images corresponding to Fig.11(b) using principal component analysis method; (d) reconstructed images corresponding to Fig.11(b) using random radiation pattern
    Schematic of imaging and recognition system[95]. (a) Collection and construction of imaging data; (b) flow chart of recognition system
    Fig. 12. Schematic of imaging and recognition system[95]. (a) Collection and construction of imaging data; (b) flow chart of recognition system
    Structural diagram of intelligence imaging system with joint optimization[96]
    Fig. 13. Structural diagram of intelligence imaging system with joint optimization[96]
    Principle diagram and application of smart metasurface[97]; (a) Application scenario of satellite communication; (b) components of smart metasurface
    Fig. 14. Principle diagram and application of smart metasurface[97]; (a) Application scenario of satellite communication; (b) components of smart metasurface
    Che Liu, Qian Ma, Lianlin Li, Tiejun Cui. Artificial Intelligence Metamaterials[J]. Acta Optica Sinica, 2021, 41(8): 0823004
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