Two-dimensional (2D) materials, due to their unique electronic, optical and structural properties, have attracted extensive attention of researchers in the world. However, most of 2D materials have low optical absorption efficiencies in the visible and near-infrared regimes, which leads to the weak light–matter interaction and limits their further applications in optoelectronic devices. Thus, enhancing the light–matter interaction of various 2D materials in the visible and near-infrared regimes, has been a key topic for many optoelectronic equipment and related applications. In this topical review, we summarized the recent developments of the 2D materials-based optical absorbers in the visible and near infrared regimes, focusing mainly on the methods and relevant physical mechanisms of several typical perfect absorbers, such as narrowband perfect absorbers, dual-band perfect absorbers, and broadband perfect absorbers. Finally, several prospective research directions from our perspectives are presented at the end.

It is well known that the inverse design of terahertz (THz) multi-resonant graphene metasurfaces by using traditional deep neural networks (DNNs) has limited generalization ability. In this article, we propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces based upon THz multi-resonant absorption spectra. The improved Transformer can obtain higher accuracy and generalization performance in the StoV (Spectrum to Vector) design compared to traditional multilayer perceptron (MLP) neural networks, while the StoI (Spectrum to Image) design achieved through CGAN can provide more comprehensive information and higher accuracy than the StoV design obtained by MLP. Moreover, the improved CGAN can achieve the inverse design of graphene metasurface images directly from the desired multi-resonant absorption spectra. It is turned out that this work can finish facilitating the design process of artificial intelligence-generated metasurfaces (AIGM), and even provide a useful guide for developing complex THz metasurfaces based on 2D materials using generative neural networks.

We numerically propose a dual-band absorber in the infrared region based on periodic elliptical graphene-black phosphorus (BP) pairs. The proposed absorber exhibits near-unity anisotropic absorption for both resonances due to the combination of graphene and BP. Each of the resonances is independently tunable via adjusting the geometric parameters. Besides, doping levels of graphene and BP can also tune resonant properties effectively. By analyzing the electric field distributions, surface plasmon resonances are observed in the graphene-BP ellipses, contributing to the strong and anisotropic plasmonic response. Moreover, the robustness for incident angles and polarization sensitivity are also illustrated.

On-demand metamaterial designs aided by artificial intelligence have hitherto received tremendous attention recently. However, the traditional deep neural networks (DNNs) still show the limited generalization ability in the inverse design of tunable graphene-based terahertz (THz) metamaterial. In this article, we propose two kinds of DNNs based on the self-attention mechanism to implement the inverse design of tunable broadband reflectors working in the THz band. Moreover, the proposed networks have been improved, so that they could adapt to different types of input vector or matrix in terms of different kinds of on-demand design requirements. Besides, adaptive batch normalization (BN) layers are introduced in our improved networks to accelerate the converging speed with low computational consumption. It could be shown in experiments that the proposed networks exhibit higher accuracy and faster convergence speed than the traditional neural networks, such as multilayer perceptron (MLP) and convolutional neural network (CNN). Finally, this work may provide a key guide for developing THz metamaterials with 2-D materials employing DNNs based on self-attention mechanism.

Light absorption of suspended monolayer graphene in the ultraviolet range is relatively low, which impedes the potential applications for graphene-based optoelectronic devices. To enhance the light-matter interaction in a wide spectral range, we propose two plasmonic structures based on a metal-dielectric-metal configuration. After rigorous optimization of geometric parameters, broadband and enhanced ultraviolet light absorption of graphene can be achieved. The bandwidth above 30% absorption rate can reach up to 245 nm (from 240 nm to 485 nm), while the relative bandwidth can reach up to 68%. The optimal absorption peak is as high as 54%. Besides, the absorption spectrum can be tuned by adjusting the geometric parameters. Physical insight is revealed with electric and magnetic field distributions. Furthermore, the tolerance for the incident angle is demonstrated, and the dependence of the polarization angle is discussed. Our approach shows promising potential for novel graphene-based applications in the ultraviolet range.