High-Resolution Programmable Metasurface Imager Based on Multilayer Perceptron Network

In the data-driven society, fidelity and accuracy of automatic decisions behind the scene rely fundamentally on a solid data or imaging acquisition system. However, conventional microwave imagers are inadequate relating to their resolution and noise capability, mainly due to the limited aperture size and rigid working principle. Here, a programmable metasurface imager with high-resolution and anti-interference performance is proposed. By implementing the structure of multilayer perceptron network in the analog domain, the metasurface-based microwave imager intelligently adapts to different datasets through illuminating a set of designed scattering patterns that mimic the feature patterns. A prototype imager system working at microwave frequency is designed and fabricated. The accuracy rate rises by 18% under the classification task of MNIST dataset, with a decline in the reconstruction imaging error. The authors experimentally demonstrate that the resolution to distinguish strip patterns goes beyond to one-fifth of the equivalent wavelength on the target plane.