Deep-Learning-Assisted Simultaneous Target Sensing and Super-Resolution Imaging.

Metasurfaces have recently experienced revolutionary progress in sensing and super-resolution imaging fields, mainly due to their manipulation of electromagnetic waves on subwavelength scales. However, on the one hand, the addition of metasurfaces can multiply the complexity of retrieving target information from detected electromagnetic fields. On the other hand, many existing studies utilize deep learning methods to provide compelling tools for electromagnetic problems but mainly concentrate on resolving one single function, limiting their versatilities. In this work, a multifunctional deep learning network is demonstrated to reconstruct diverse target information in a metasurface-target interactive system. First, a preliminary experiment verifies that the metasurface-involved scenario can tolerate the system noises. Then, the captured electric field distributions are fed into the multifunctional network, which can not only accurately sense the quantity and relative permittivity of targets but also generate super-resolution images precisely. The deep learning network, thus, paves an alternative way to recover the targets' information in metasurface-target interactive systems, accelerating the progression of target sensing and superimaging areas. Besides, another new network that allows forward electromagnetic prediction is also proposed and demonstrated. To sum up, the deep learning methodology may hold promise for inverse reconstructions or forward predictions in many electromagnetic scenarios.

Spin-decoupled excitation and wavefront shaping of structured surface waves via on-chip terahertz metasurfaces.

Surface waves (SWs) are of great importance in terahertz (THz) photonics applications due to their subwavelength properties. Hence, it is crucial to develop surface wavefront shaping techniques, which is urgent in modern information technologies. In this paper, a new scheme is proposed to realize SW excitation and spin-decoupled wavefront shaping with an ultracompact planar meta-device working in the THz range. The meta-device is composed of two parts: meta-atoms (in the center) and plasmonic metals (on the left and right sides). By carefully setting the geometry size and rotation angle of each meta-atom, the encoded spin-decoupled phase distributions for both left circularly polarized (LCP) and right circularly polarized (RCP) incident THz waves are determined. In this way, circularly polarized (CP) incident THz waves can be converted to SWs propagating along plasmonic metals with unique wavefront profiles, i.e., Bessel and focusing profiles. Full-wave simulations and THz near-field scanning experiments were performed to verify the functionalities of the meta-device, both of which are in great agreement with theoretical predictions. Our findings may provide more solutions to design THz integrated photonic devices and systems.