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Li R C, Zou Y J, Chen T H, et al. Broadband achromatic metasurface holography[J]. Opto-Electron Eng, 2023, 50(8): 230118. doi: 10.12086/oee.2023.230118
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Broadband achromatic metasurface holography
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Abstract
Aiming at the problems of narrow working frequency band and low near field imaging efficiency in metasurface holographic imaging technology, this paper proposed the principle and model of optimization of achromatic broadband metasurface hologram imaging. A deep learning network model based on the depth image prior (DIP) is proposed for single-target passive metasurface hologram design, and achromatic broadband metasurface hologram imaging is achieved. Numerical simulation and experimental results have proved that the designed holographic imaging device can achieve good achromatic imaging effect in the 9 GHz~11 GHz frequency band, and has great potential application in the field of holographic imaging and broadband functional device design.-
Keywords:
- holographic imaging /
- metasurface /
- achromatic
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References
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Overview
Computational holography digitizes the whole holographic process by computer, which greatly improves the accuracy and flexibility of imaging. It can realize the display of real or virtual objects without the limitation of the light source, which significantly expands the application field of holography. Metasurface is a two-dimensional planar form of metamaterial, which further increases the degree of freedom by introducing the concept of “macroscopic order” based on the use of structural parameters to control electromagnetic waves, and has the advantages of low material loss and simple processing. Due to its excellent modulation properties, the matesurface is well suitable as a wavefront encoding material for computing holograms, and the combination of metasurface and holographic imaging technology has become one of the current research hotspots in nanotechnology and electromagnetics. However, there are still problems such as low near-field imaging efficiency and narrow frequency band in the metasurface holographic imaging to restrict the practicalization of the metasurface holographic imaging. Aiming at the above problems, a design method of an achromatic broadband metasurface holographic imaging device based on Depth Image Prior (DIP) is proposed in this paper. Firstly, the phase feature vector of the central operating frequency is generated by the convolutional neural networks. Based on the structural dispersion of the actual metasurface elements, the phase feature vector in the working frequency band is also generated. Finally, the frequency band reconstruction image is generated by Rayley-Sommerfeld. The holographic phase map is obtained by the output of the deep convolutional neural network. High-quality reconstructed images can be generated after 20,000 iterations. The reflection cross-polarization unit is used as an example to verify the theoretical algorithm model in this paper. The holographic phase diagram of the network output was discretized at intervals of 10°, and MATLAB and CST co-simulation were used for rapid modeling. Numerical simulation results prove that the designed holographic imaging device can achieve a good achromatic imaging effect in the 9 GHz~11 GHz frequency band. The near-field measured results of bare object and cloak with a wideband frequency signal (8 GHz ~ 12 GHz) via Vector Network Analyzer (VNA) at 9 GHz, 9.5 GHz, 10 GHz, 10.5 GHz, and 11 GHz by plane wave illumination. The difference between experimental results and numerical simulation results is mainly caused by experimental errors and PCB machining errors. In general, relatively clear imaging can be observed in the design bandwidth range of 9 GHz~11 GHz. It has great potential applications in the field of holographic imaging and broadband functional device design.
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Li R C, Zou Y J, Chen T H, et al. Broadband achromatic metasurface holography[J]. Opto-Electron Eng, 2023, 50(8): 230118. doi: 10.12086/oee.2023.230118
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Figure 1.
Dispersion effects on imaging devices. (a) Imaging targets; (b) Holographic phase distribution; (c-e) Reconstruction images at 9 GHz, 10 GHz, and 11 GHz
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Figure 2.
A broadband dispersion metasurface holographic imaging network model based on the depth image priors (DIP)
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Figure 3.
Network training process. (a) The change of network training loss value with the number of iterations; (b) Numerical reconstruction image outputs by the network under different iterations
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Figure 4.
Reflective cross-polarized rotational metasurface element. (a) Three-dimensional schematic diagram of the cross-polarization conversion unit; (b) Top-floor metal structure; (c) and (d) Polarization conversion efficiency and phase
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Figure 5.
Electric field intensity distributions of broadband dispersion metasurface holographic imaging device at 150 mm
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Figure 6.
Distributions of electric field intensity experimentally measured by a broadband dispersion metasurface holographic imaging device at 150 mm
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