A programmable metasurface with dynamic polarization, scattering and focusing control

Abstract

Diverse electromagnetic (EM) responses of a programmable metasurface with a relatively large scale have been investigated, where multiple functionalities are obtained on the same surface. The unit cell in the metasurface is integrated with one PIN diode, and thus a binary coded phase is realized for a single polarization. Exploiting this anisotropic characteristic, reconfigurable polarization conversion is presented first. Then the dynamic scattering performance for two kinds of sources, i.e. a plane wave and a point source, is carefully elaborated. To tailor the scattering properties, genetic algorithm, normally based on binary coding, is coupled with the scattering pattern analysis to optimize the coding matrix. Besides, inverse fast Fourier transform (IFFT) technique is also introduced to expedite the optimization process of a large metasurface. Since the coding control of each unit cell allows a local and direct modulation of EM wave, various EM phenomena including anomalous reflection, diffusion, beam steering and beam forming are successfully demonstrated by both simulations and experiments. It is worthwhile to point out that a real-time switch among these functionalities is also achieved by using a field-programmable gate array (FPGA). All the results suggest that the proposed programmable metasurface has great potentials for future applications.

Figure 1. Proposed unit cell and its coding property.

(a) Schematic of proposed unit cell integrated with one PIN diode. The detailed dimensions are px = 5.8 mm, py = 4.9 mm, h = 1.58 mm. (b) Proposed unit cell with actual biasing architecture. The biasing circuit is elaborately designed to isolate the direct-current (DC) and radio frequency (RF) signals. (c,d) The equivalent circuits of the PIN diode (MACOM MADP-000907-14020) at ON and OFF states, respectively. (e) Reflection amplitudes. (f) Reflection phases. (g) Reflection phase differences.

Figure 2. Schematic view of multiple functions for proposed metasurface.

Figure 3. Flowchart of genetic algorithm (GA) based optimization for various functions.

Figure 4. Reconfigurable polarization property.

(a) The proposed metasurface with normal incident plane electromagnetic wave polarized by 45° with respect to the x axis. The metasurface is composed of the proposed unit cells periodically arranged in a square lattice. (b) Local zoom of the metasurface and the decomposition of incident electric field. (c) Simulated reflection coefficients. (d) All ‘1’ coding and sketch map of polarization unaltered reflection. (e) All ‘0’ coding and sketch map of polarization rotated reflection.

Figure 5. Diversified scattering properties with plane wave incidence.

(a) The proposed metasurface illuminated by an x-polarized plane wave. (b–d) Regular binary coding matrices and the corresponding anomalous scattering patterns. (e–g) Optimized irregular binary coding matrices and the corresponding scattering patterns. (h) Convergence curve of GA-based optimization for (f).

Figure 6. Dynamic focusing properties with a point source.

(a) The proposed metasurface illuminated by a horn antenna to imitate a point source. (b–d) Binary coding matrices and the corresponding steering focused beams pointed at 0°, 20° and 40°, respectively. (e–g) Binary coding matrices and the corresponding shaped beams.

Figure 7. Fabricated prototype and experiments.

(a) Proposed metasurface constructed by 5 identical sub-metasurfaces. (b) FPGA control board and the sub-metasurfaces. (c) Local zoom of a sub-metasurface without solder mask and soldering. (d) Local zoom of the back of a sub-metasurface. (e) Measurement setup for reconfigurable polarization and agile scattering performance. (f) Measurement setup for beam-focusing and beam-shaping performance.

Figure 8. Measurement of reconfigurable polarization conversion.

(a) Reflection coefficients when all the unit cells work at “1” states (all 1 coding). (b) Reflection coefficients when all the unit cells work at “0” states (all 0 coding).

Figure 9. Measurement of RCS reduction versus frequency.

The proposed metasurface and a metallic plate with the same size are measured, respectively, and then the RCS reduction is obtained by subtracting the results. (a) Measured and simulated RCS reduction with chess-board coding shown in Fig. 5(d). (b) Measured and simulated RCS reduction with optimized diffusion coding shown in Fig. 5(f).

Figure 10. Measurement of beam focusing and beam steering performance.

(a) Measured 3-dimensional focused beam. (b) Measured and simulated focused beams pointed at (0°, 0°). (c) Measured steering beams in xoz plane. The corresponding coding matrices and the simulated results are presented in Fig. 6(b–d), respectively.

Figure 11. Measurement of beam forming performance.

(a) Broad beam. (b) Cosecant shaped beam. (c) Triple-beam. The corresponding coding matrices and the simulated results are presented in Fig. 6(e–g), respectively.