Deep-learning based broadband reflection reduction metasurface

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]
卷期号:31 (9): 14593-14593 被引量:8
标识
DOI:10.1364/oe.486096
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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