Zhu Ruichao, Qiu Tianshuo, Wang Jiafu, Sui Sai, Hao Chenglong, Liu Tonghao, Li Yongfeng, Feng Mingde, Zhang Anxue, Qiu Cheng-Wei, Qu Shaobo
Department of Basic Sciences, Air Force Engineering University, Xi'an, People's Republic of China.
Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
Nat Commun. 2021 May 20;12(1):2974. doi: 10.1038/s41467-021-23087-y.
Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 2 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.
超表面为操纵电磁波提供了前所未有的自由度。在超表面设计中，大量的超原子必须进行优化以产生所需的相位分布，这既耗时又有时难以实现。在本文中，我们提出了一种基于迁移学习的快速准确的功能性超表面逆设计方法，该方法可以根据特定功能的输入相位分布整体生成超表面图案。基于GoogLeNet-Inception-V3的迁移学习网络可以预测两个超原子的相位，准确率约为90%。该方法通过使用训练好的网络进行功能性超表面设计得到验证。整体生成超表面图案以实现两种典型功能，即二维聚焦和异常反射。仿真和实验均验证了高设计精度。该方法为快速功能性超表面设计提供了一种逆设计范式，并且可以很容易地用于建立具有全相位跨度的超原子库。
