An Sensong, Zheng Bowen, Shalaginov Mikhail Y, Tang Hong, Li Hang, Zhou Li, Ding Jun, Agarwal Anuradha Murthy, Rivero-Baleine Clara, Kang Myungkoo, Richardson Kathleen A, Gu Tian, Hu Juejun, Fowler Clayton, Zhang Hualiang
Opt Express. 2020 Oct 12;28(21):31932-31942. doi: 10.1364/OE.401960.
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom's wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface.
超表面在塑造光波前方面已展现出巨大潜力，且与庞大的几何光学器件相比，其结构更为紧凑。超原子作为超表面的基本构建单元，其设计通常依靠反复试验来实现目标电磁响应。这一过程包括对大量具有不同物理和几何参数的超原子设计进行表征，这需要巨大的计算资源。本文介绍了一种基于深度学习的超表面/超原子建模方法，在保持准确性的同时显著减少表征时间。基于卷积神经网络（CNN）结构，所提出的深度学习网络能够对具有近乎自由形式的二维图案以及不同晶格尺寸、材料折射率和厚度的超原子进行建模。此外，该方法能够在毫秒级时间尺度内预测超原子的宽频谱响应，这对于需要快速按需设计和优化超原子/超表面的应用具有吸引力。
