宽带
计算机科学
光学
带宽(计算)
极化(电化学)
电信
物理
物理化学
化学
作者
Haiyan Xie,Xiuli Yue,Kaihuai Wen,Difei Liang,Tiancheng Han,Longjiang Deng
出处
期刊:Optics Express
[The Optical Society]
日期:2023-02-27
卷期号:31 (9): 14593-14593
被引量:8
摘要
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<-10 dB) and 107.4% (reflection<-15 dB), which demonstrates a great advantage in bandwidth compared with the previous designs.
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