RESUMO
Reflection reduction metasurface (RRM) has been drawing much attention due to its potential application in stealth technology. However, the traditional RRM is designed mainly based on trial-and-error approaches, which is time-consuming and leads to inefficiency. Here, we report the design of a broadband RRM based on deep-learning methodology. On one hand, we construct a forward prediction network that can forecast the polarization conversion ratio (PCR) of the metasurface in a millisecond, demonstrating a higher efficiency than traditional simulation tools. On the other hand, we construct an inverse network to immediately derive the structure parameters once a target PCR spectrum is given. Thus, an intelligent design methodology of broadband polarization converters has been established. When the polarization conversion units are arranged in chessboard layout with 0/1 form, a broadband RRM is achieved. The experimental results show that the relative bandwidth reaches 116% (reflection<-10â dB) and 107.4% (reflection<-15â dB), which demonstrates a great advantage in bandwidth compared with the previous designs.
RESUMO
We propose a radar-infrared bi-stealth rasorber that not only provides broad microwave absorptivity and low infrared emissivity but also possesses a microwave transmission window at low frequency. It is composed of three functional layers, which are carefully designed to independently control the infrared emission, microwave absorption, and transmission, respectively. The structure exhibits broadband (8.1-19.3â GHz) and high-efficiency (>90%) absorption. A transmission window appears at low frequency with a transmission peak of 80% at 2.68â GHz. The thermal emissivity of the structure is about 0.27 in the atmosphere window, which is close to that of metal. Moreover, the total thickness of the proposed structure is only 3.713â mm. The low-infrared-emissivity, high-microwave-absorption and frequency-selective-transmission properties promise it will find potential applications in various stealth fields.