High-efficiency broadband polarization-independent superscatterer using conformal metasurfaces

Safe detection of an arbitrarily shaped platform is critical for survivability, rescue, or navigation safety in a remote region. Metasurfaces afford great potential due to their strong electromagnetic(EM) wave control. However,studies have mainly focused on the physics and design of metasurfaces on planar plates, which does not satisfy the current requirements of aerodynamics and aesthetics. Herein, we propose a sophisticated strategy to design a metasurface that can wrap over arbitrarily shaped objects with moderate curvature on which optical aberrations are commonly introduced. By designing each meta-atom on the basis of the required position and phase compensation, exact EM wavefronts are restored. For verification, several conformal metasurfaces were designed and numerically studied on metallic cylinders at the microwave spectrum. A proof-of-concept device is fabricated and is experimentally characterized. The results demonstrate the availability of the desirable dual-beam superscatterer with strong backscattering enhancement toward two directions, thus indicating that the distortions induced by an arbitrary platform can be efficiently corrected. Our method affords an efficient alternative for designing highperformance multifunctional optoelectronic devices equipped on a moderately curved platform.

Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network

Intelligent coding metasurface is a kind of information-carrying metasurface that can manipulate electromagnetic waves and associate digital information simultaneously in a smart way.One of its widely explored applications is to develop advanced schemes of dynamic holographic imaging.By now,the controlling coding sequences of the metasurface are usually designed by performing iterative approaches,including the Gerchberg–Saxton(GS)algorithm and stochastic optimization algorithm,which set a large barrier on the deployment of the intelligent coding metasurface in many practical scenarios with strong demands on high efficiency and capability.Here,we propose an efficient non-iterative algorithm for designing intelligent coding metasurface holograms in the context of unsupervised conditional generative adversarial networks(cGANs),which is referred to as physics-driven variational auto-encoder(VAE)cGAN(VAE-cGAN).Sharply different from the conventional cGAN with a harsh requirement on a large amount of manual-marked training data,the proposed VAE-cGAN behaves in a physics-driving way and thus can fundamentally remove the difficulties in the conventional cGAN.Specifically,the physical operation mechanism between the electric-field distribution and metasurface is introduced to model the VAE decoding module of the developed VAE-cGAN.Selected simulation and experimental results have been provided to demonstrate the state-of-the-art reliability and high efficiency of our VAE-cGAN.It could be faithfully expected that smart holograms could be developed by deploying our VAE-cGAN on neural network chips,finding more valuable applications in communication,microscopy,and so on.