由于超材料和超表面的亚波长结构单元的形状和尺寸具有很大的设计自由度,可对电磁波的振幅、相位、波前和方向等进行复杂而精确的调控,同时随着结构参数数量的增加,结构设计的时间往往呈指数增长。提出了一种基于反向传播(BP)神经网络快速优化超表面结构的方法,实现了兼具高衍射效率、宽带宽和高角色散等优势的太赫兹介质超光栅。利用有限次数的严格耦合波分析建立的数据集来训练BP神经网络,可准确预测任意结构参数的超光栅衍射光谱,并通过遍历所有结构参数快速筛选出具有最高衍射效率且宽带宽的超光栅,相比传统的遍历计算方法速度提高了一万倍,证明了基于BP神经网络的超表面优化方法的高效性以及精准性,同时为太赫兹波段提供了一种性能优异的衍射元件。

Metamaterials and metasurfaces show great potentials to adjust the amplitude, phase, wavefront and direction of electromagnetic waves in a complex and precise manner, since the shape and size of the subwavelength unit can be designed with large degree of freedom. At the same time, with the increase of the number of structural parameters involved, the structural design time increases in an exponential way. This paper proposes a method for the fast optimization of metasurface structures based on the back-propagation (BP) neural network, and a terahertz dielectric metagrating with the merits of high diffraction efficiency, wide bandwidth, and high angular dispersion is achieved. A dataset established via a limited number of rigorous coupled wave analyses is used to train the BP neural network. It can accurately predict the diffraction spectrum of the metagrating with an arbitrary geometry. Simultaneously, the metagrating with the highest diffraction efficiency and wide bandwidth is fast selected by quickly traversing all structural parameters. The designed speed is increased by 10,000 times compared with that of the traditional traversing calculation method, which proves the high efficiency and accuracy of the metasurface optimization method based on the BP neural network. The study provides a diffractive element with excellent performance for terahertz applications.