Tao Zilong, You Jie, Zhang Jun, Zheng Xin, Liu Hengzhu, Jiang Tian
Opt Lett. 2020 Mar 15;45(6):1403-1406. doi: 10.1364/OL.386980.
Here, a deep learning (DL) algorithm based on deep neural networks is proposed and employed to predict the chiroptical response of two-dimensional (2D) chiral metamaterials. Specifically, these 2D metamaterials contain nine types of left-handed nanostructure arrays, including U-like, T-like, and I-like shapes. Both the traditional rigorous coupled wave analysis (RCWA) method and DL approach are utilized to study the circular dichroism (CD) in higher-order diffraction beams. One common feature of these chiral metamaterials is that they all exhibit the weakest intensity but the strongest CD response in the third-order diffracted beams. Our work suggests that the DL model can predict CD performance of a 2D chiral nanostructure with a computational speed that is four orders of magnitude faster than RCWA but preserves high accuracy. The DL model introduced in this work shows great potentials in exploring various chiroptical interactions in metamaterials and accelerating the design of hypersensitive photonic devices.
在此，提出并应用了一种基于深度神经网络的深度学习（DL）算法来预测二维（2D）手性超材料的旋光响应。具体而言，这些二维超材料包含九种类型的左手性纳米结构阵列，包括U形、T形和I形。传统的严格耦合波分析（RCWA）方法和DL方法都被用于研究高阶衍射光束中的圆二色性（CD）。这些手性超材料的一个共同特征是，它们在三阶衍射光束中都表现出最弱的强度但最强的CD响应。我们的工作表明，DL模型能够以比RCWA快四个数量级的计算速度预测二维手性纳米结构的CD性能，同时保持高精度。本文中引入的DL模型在探索超材料中的各种旋光相互作用以及加速高灵敏度光子器件的设计方面显示出巨大潜力。
