Mu Hua, Zhang Yu, Liang Zhenyu, Gao Haoqi, Xu Haoli, Wang Bingwen, Wang Yangyang, Yang Xing
State Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Nanomaterials (Basel). 2024 Dec 8;14(23):1973. doi: 10.3390/nano14231973.
Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters.
超构透镜可以通过控制入射波的幅度、相位和偏振来实现任意光调制，并已应用于各个领域。本文提出了一种基于超构透镜设计的颜色路由器，能够有效地分离从可见光到近红外光的光谱。传统的超构透镜设计方法需要大量的模拟，耗时较长。在本研究中，我们提出了一种能够在宽波长范围内进行正向预测的深度学习网络，并结合粒子群优化算法来高效设计超构透镜。模拟结果与理论预测紧密吻合。所设计的颜色路由器能够同时满足目标光谱（特别是红色、绿色、蓝色和近红外光）的理论传输相位，并将它们聚焦到指定区域。值得注意的是，这种设计的光学效率达到40%，显著超过了传统滤色器的效率。
