Absorption and Diffusion Enabled Ultrathin Broadband Metamaterial Absorber Designed by Deep Neural Network and PSO

Abstract

With absorption and interference cancellation, lossy metamaterials can achieve broadband electromagnetic wave absorption. However, the design of such metamaterial absorbers (MMAs) is very complicate, because tremendous meta-atoms and their configuration parameters need to be determined. The conventional methods, such as parameter sweep and adjoint-based optimization, suffer from slow convergence and local minimum problem. In this letter, a deep neural network (DNN) is used to map the configuration parameters of a type of meta-atom onto its reflection coefficients. The DNN well predicts the reflection coefficients and is applied to design the MMA under the demand of −10 dB backscattering reduction (BSR) covering the microwave S to Ku bands with the smallest thickness. By globally searching the configuration parameter space using particle swarm optimization (PSO) algorithm, which automatically cooperates the absorption and phase cancelation (or diffusion) of meta-atoms, we obtain the optimized configuration of the meta-atoms and corresponding filling ratios. The designed MMA realizes −10 dB absorption bandwidth covering 2.2–18 GHz with the thickness only 4 mm, which is further verified by experiments. The performance of our absorber is better than other similar absorbers reported. Our letter provides a useful method for ultrathin broadband MMA design, which can also be applied to other functional devices based on metamaterials.