Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian
State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, 410073, Changsha, China.
Defense Innovation Institute, Academy of Military Sciences PLA China, 100071, Beijing, China.
Nanophotonics. 2022 Sep 6;11(20):4465-4478. doi: 10.1515/nanoph-2022-0310. eCollection 2022 Sep.
A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations. Firstly, some "pseudo data" are generated by a forward prediction network that can efficiently predict the circular dichroism (CD) response of 2D diffractive chiral metamaterials to reinforce the dataset after necessary denoising. Then, the algorithm uses the CD spectra and the predictions of parameters with smaller errors iteratively to achieve accurate values of the remaining parameters. Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. Furthermore, the fast inverse design of multiple shaped metamaterials allows for different light manipulation, demonstrating excellent potentials in applications of optical coding and information processing. This work belongs to one of the first attempts to thoroughly characterize the flexibility, interpretability, and generalization ability of DEIFS algorithm in studying various chiroptical effects in metamaterials and accelerating the inverse design of hypersensitive photonic devices.
提出了一种数据增强迭代少样本（DEIFS）算法，以实现多形状二维手性超材料的精确高效逆设计。具体而言，利用传统的严格耦合波分析（RCWA）方法和DEIFS算法，研究了三类具有不同几何参数（包括宽度、间隔空间、桥长和金长度）的二维衍射手性结构，前者辅助后者的训练过程。DEIFS算法可分为两个主要阶段，即数据增强和迭代。首先，通过前向预测网络生成一些“伪数据”，该网络可以有效地预测二维衍射手性超材料的圆二色性（CD）响应，在进行必要的去噪后增强数据集。然后，该算法使用CD光谱和误差较小的参数预测值进行迭代，以获得其余参数的准确值。同时，根据几何参数对旋光响应的影响，增加了一项新功能，从数据角度解释DEIFS算法的实验结果，提高了DEIFS的可解释性。通过这种方式，DEIFS算法用一种更快、更简单的方法取代了耗时的迭代优化过程，该方法用数量比大多数以前的深度学习方法至少少一到两个数量级的数据集实现了精确的逆设计，减少了对模拟光谱的依赖。此外，多种形状超材料的快速逆设计允许进行不同的光操纵，并在光学编码和信息处理应用中展现出优异的潜力。这项工作属于首次尝试，旨在全面表征DEIFS算法在研究超材料中各种旋光效应以及加速超灵敏光子器件逆设计方面的灵活性、可解释性和泛化能力。
