The design of a multi-layer coding metamaterial absorber (MCMMA) is a multi-objective problem for maximizing the electromagnetic absorption bandwidth (EAB) and minimizing the total thickness (TT) by selecting the optimum variables such as the configuration parameters and the resistive film distribution of the metamaterial unit cell. Empirical methods and parameter sweeping have local search problems and converge slowly. In this letter, a multi-objective optimization (MOO) method is proposed for a low-profile and broadband MCMMA design. A topological encoding strategy is presented for the resistive film distribution where a 10×10-pixel array is divided into 5 parts with the real-valued representation. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) is employed to globally search for the combination of the resistive film distribution, geometrical, and material parameters. With an EAB of 142.68%, the designed MCMMA realizes microwave absorption bandwidth covering 7.24-43.28 GHz with only 4.05 mm, which is further fabricated and measured. The experimental result agrees well with the simulation result. This letter provides a valuable method for designing a low-profile and broadband MCMMA, thereby paving the way for coding metamaterials.

Multi-objective optimization (MOO) for the microwave metamaterial absorber (MMA) normally adopts evolutionary algorithms, and these optimization algorithms require many objective function evaluations. To remedy this issue, a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions. An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations. Firstly, new sample points are generated by the MOO on surrogate models. Then, new samples are captured by exploiting each objective function. Furthermore, a weighted sum of the improvement of hypervolume (IHV) and the distance to sampled points is calculated to select the new sample. Compared with two well-known MOO algorithms, the proposed algorithm is validated by benchmark problems. In addition, two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.

The multi-layer metamaterial absorbers (MMAs) are designed by using Kriging surrogate model. By selecting different metamaterial models from the metamaterial database, metamaterial absorbers used in different wavebands can be designed quickly. In this paper, the six MMAs are designed through kriging model. Their absorptivity greater than 90% has been achieved at the important radar band within 4-18GHz respectively.

Magnetic metal absorbing materials have exhibited excellent absorptance performance. However, their applications are still limited in terms of light weight, low thickness and wide absorption bandwidth. To address this challenge, we design a broadband and low-profile multilayer absorber using cobalt–iron (CoFe) alloys doped with rare earth elements (REEs) lanthanum (La) and Neodymium (Nd). An improved estimation of distribution algorithm (IEDA) is employed in conjunction with a mathematical model of multilayer absorbing materials (MAMs) to optimize both the relative bandwidth with reflection loss (RL) below −10 dB and the thickness. Firstly, the absorption performance of CoFe alloys doped with La/Nd with different contents is analysed. Subsequently, IEDA is introduced based on a mathematical model to achieve an optimal MAM design that obtains a balance between absorption bandwidth and thickness. To validate the feasibility of our proposed method, a triple-layer MAM is designed and optimized to exhibit wide absorption bandwidth covering C, X, and Ku bands (6.16–12.82 GHz) and a total thickness of 2.39 mm. Then, the electromagnetic (EM) absorption mechanisms of the triple-layer MAMs are systematically investigated. Finally, the triple-layer sample is further fabricated and measured. The experimental result is in good agreement with the simulated result. This paper presents a rapid and efficient optimization method for designing MAMs, offering promising prospects in microwave applications, such as radar-stealth technology, EM shielding, and reduced EM pollution for electronic devices.

As we know, the thickness mainly affects the yield of low-temperature co-fired ceramics (LTCC) device. There is a high risk of cracking for the thick structure in manufacture process especially for the curved one. In this paper, double-layer artificial magnetic conductors (AMCs), mixed feeding network and built-in wide-band laminated waveguide (LWG) with rectangular waveguide (RWG) transition are specially analyzed and designed. These structures all have low-profile characteristics. Thus, they are suitable for conformal LTCC process. The double-layer AMCs are presented to enhance the bandwidth and the gain of a Ka-band patch element. The thin mixed network consisting of microstrip line (MSL) and LWG is used to replace the thick multistage LWG network. An improved LWG-RWG transition integrated in LWG layer is designed to possess wider bandwidth. At last, the low-profile conformal 16×16 antenna array is designed, manufactured and tested. The measured results demonstrate that the low-profile conformal 16×16 antenna array has a good and stable performance with an enhanced impendence bandwidth of 12.17% and a high gain up to 26.9dB.

In this paper, a deep learning method is proposed to predict the absorption of metamaterial absorber (MMA) in real time, and then the Binary Particle Swarm Optimization (BPSO) method is introduced to cooperate with the deep learning prediction model to design MMA in a few seconds. The experimental result shows that the proposed method in this paper can achieve an absorption above 90% over the broadband of 7.0-16.8GHz with a prediction accuracy of 96.47%.