Wang Jingjing, Chen Sixue, Qiu Yihang, Chen Xiaoying, Shen Jian, Li Chaoyang
School of Electronic Science and Technology, Hainan University, Haikou 570228, China.
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China.
Micromachines (Basel). 2023 Oct 13;14(10):1925. doi: 10.3390/mi14101925.
Chiral metasurfaces have garnered significant interest as an emerging field of metamaterials, primarily due to their exceptional capability to manipulate phase distributions at interfaces. However, the on-demand design of chiral metasurface structures remains a challenging task. To address this challenge, this paper introduces a deep learning-based network model for rapid calculation of chiral metasurface structure parameters. The network achieves a mean absolute error (MAE) of 0.025 and enables the design of chiral metasurface structures with a circular dichroism (CD) of 0.41 at a frequency of 1.169 THz. By changing the phase of the chiral metasurface, it is possible to produce not only a monofocal lens but also a multifocal lens. Well-designed chiral metasurface lenses allow us to control the number and position of focal points of the light field. This chiral metasurface, designed using deep learning, demonstrates great multifocal focus characteristics and holds great potential for a wide range of applications in sensing and holography.
手性超表面作为超材料的一个新兴领域已引起广泛关注，主要是因为其在界面处操纵相位分布的卓越能力。然而，手性超表面结构的按需设计仍然是一项具有挑战性的任务。为应对这一挑战，本文引入了一种基于深度学习的网络模型，用于快速计算手性超表面结构参数。该网络的平均绝对误差（MAE）为0.025，并能够设计出在1.169太赫兹频率下圆二色性（CD）为0.41的手性超表面结构。通过改变手性超表面的相位，不仅可以制造出单焦点透镜，还可以制造出多焦点透镜。精心设计的手性超表面透镜使我们能够控制光场焦点的数量和位置。这种利用深度学习设计的手性超表面展现出出色的多焦点聚焦特性，在传感和全息术等广泛应用中具有巨大潜力。
