Abstract
Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM’s design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces.
Highlights
M ETAMATERIALS (MMs) are artificial engineering media which consist of periodic or nonperiodic geometric arrays with sub-wavelength unit cells
This unit cell composed of three layers: on the top is the patterned resistive film with a surface resistance of rΩ/sq which printed on the 0.175mm thick Polyethylene Terephthalate (PET) sheets, in the middle is the polymethacrylimide (PMI) foam, and the bottom layer is a 0.175mm thick PMI sheet covered with the resistive film
The design process is implemented with the feature transformer neural network (FTNN) and the generator neural network (GNN) which is exhibited in the blue area of Fig
Summary
M ETAMATERIALS (MMs) are artificial engineering media which consist of periodic or nonperiodic geometric arrays with sub-wavelength unit cells. MMs have demonstrated their unprecedented capability in manipulating the properties of electromagnetic (EM) waves including amplitude, phase, polarization and etc. Their exotic properties mainly arise from geometry details and constituting material of the unit cell. The specific feature of MMs leads to many novel applications such as perfect metamaterial absorber (MMA) [1], metalens [2], [3], coding metasurface [4], holographic imaging [5] and EM shielding [6]–[9]. These novel devices have been widely used in the areas of bolometers [16], radar cross section reduction [17], imaging devices [18], EM compatibility [19], sensing [20]–[22], and EM interference shielding [23]
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