Absorbers are widely used in diverse applications such as antenna pattern shaping, stealth technology and mid-infrared converters. Recently, metamaterials have been introduced to design microwave absorbers [1]. Enabled by graphene’s electrical tunability, THz absorbers have also been developed [2]. Most designs are limited to concept proofs and, as we will see, are not ultimately optimized [3].
In this communication, we propose the use of Genetic Algorithm (GA) to find optimal topologies for metamaterial-like absorbers with graphene-based unit cells. The main idea is to break down the unit cell into a matrix of pixels and use the GA to iteratively improve the topology. The GA is coupled to a full-wave solver to evaluate the absorption bandwidth (BW), and the topology evolves until an optimal solution is reached.
We first prove the validity of the idea by optimizing an already existing design. We take [3] as an example and maintain its chemical potential, relaxation time and dimensions. Then, we apply our approach to find a topology that maximizes the 90% absorption BW. Fig. 1 shows that the 90% absorption BW increases from the original 39% to our 59%, with a unit cell topology reassembling the original ring resonator.
We secondly prove the versatility of the GA-based methodology by designing a microwave absorber from scratch. In this case, we assume a lossless substrate (e.g. air) to model the worst-case scenario for an absorber. Fig. 2 shows the design of an absorber with a 1.2-mm thick lossless substrate and a PEC ground plane. Even in such adverse conditions, our method achieves a BW of 65%. It is worth noting that, with lithographic methods, fabrication of such apparently complex patterns is possible.
This method is executable for designing not only ultra-wideband absorbers, but also for any other metamaterial. Getting advantage of tunable materials like graphene for the control of unit cell, along with the GA, diverse objectives such as cloaking and beam steering can be approached optimally.
[1] H. Taghvaee, et al., ICEEM 2014.
[2] HR. Taghvaee, et al., Opt. Commun. 383, 11-16, 2017.
[3] Chang Liu, et al., AIP Advances 8, 015301, 2018.
