材料科学
红外线的
多光谱图像
辐射传输
激光器
等离子体子
镜面反射
热的
反射(计算机编程)
光子学
光电子学
光学
纳米光子学
计算机科学
纳米技术
人工智能
物理
气象学
程序设计语言
作者
Xianghui Liu,Pan Wang,Chengyu Xiao,Liucheng Fu,Jun Xu,Di Zhang,Han Zhou,Tongxiang Fan
标识
DOI:10.1002/adfm.202212068
摘要
Abstract Metasurface‐based mid‐infrared stealth compatible with visual or laser provide a promising way to increase survivability of military installations. However, current designs of metasurfaces following traditional paradigm suffer from low efficiency on calculating global structural parameters for multispectral requirements and limited thermal radiation engineering. Here, a metasurface with high‐performance compatible stealth and effect thermal management is proposed, based on a machine‐learning‐enabled inverse design approach. The approach can rapidly generate multiple non‐unique solutions in global to match the desired spectra in multi‐wavebands, utilizing neural networks with physical‐based data dimensionality. The finally generated metasurface has low specular reflectance (<0.01) at near‐infrared laser wavelength and enhanced broad absorptance peak at 5–8 µm, which is attributed to the reflection splitting effect and infrared plasmonic resonance, respectively. Furthermore, a low‐cost fabrication method is developed to produce the metasurface by colloidal lithography. The metasurface is demonstrated to have excellent capability of radiative thermal control and significantly decrease the apparent temperature under thermal imager (>50 °C). This study reveals an opportunity to inversely generate multiple solutions for photonic structures targeting on multispectral responses, in a systematic and efficient manner.
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