Wei Xueling, Zhao Fen, Zhang Yiyi, Nong Jie, Huang Jie, Zhang Zhaojian, Chen Huan, Zhang Zhenfu, He Xin, Yu Yang, Zhang Zhenrong, Yang Junbo
Opt Express. 2023 Oct 23;31(22):35653-35669. doi: 10.1364/OE.499033.
In recent years, structural color has developed rapidly due to its distinct advantages, such as low loss, high spatial resolution and environmental friendliness. Various inverse design methods have been extensively investigated to efficiently design optical structures. However, the optimization method for the inverse design of structural color remains a formidable challenge. Traditional optimization approaches, such as genetic algorithms require time-consuming repetitions of structural simulations. Deep learning-assisted design necessitates prior simulations and large amounts of data, making it less efficient for systems with a small number of features. This study proposes a tensor completion algorithm capable of swiftly and accurately predicting missing datasets based on partially obtained datasets to assist in structural color design. Transforming the complex physical problem of structural color design into a spatial structure relationship problem linking geometric parameters and spectral data. The method utilizes tensor multilinear data analysis to effectively capture the complex relationships associated with geometric parameters and spectral data in higher-order data. Numerical and experimental results demonstrate that the algorithm exhibits high reliability in terms of speed and accuracy for diverse structures, datasets of varying sizes, and different materials, significantly enhancing design efficiency. The proposed algorithm offers a viable solution for inverse design problems involving complex physical systems, thereby introducing a novel approach to the design of photonic devices. Additionally, numerical experiments illustrate that the structural color of cruciform resonators with diamond can overcome the high loss issues observed in traditional dielectric materials within the blue wavelength region and enhance the corrosion resistance of the structure. We achieve a wide color gamut and a high-narrow reflection spectrum nearing 1 by this structure, and the theoretical analysis further verifies that diamond holds great promise in the realm of optics.
近年来，结构色因其低损耗、高空间分辨率和环境友好等显著优势而迅速发展。人们广泛研究了各种逆向设计方法，以高效设计光学结构。然而，结构色逆向设计的优化方法仍然是一项艰巨的挑战。传统的优化方法，如遗传算法，需要耗时地重复进行结构模拟。深度学习辅助设计需要先验模拟和大量数据，对于具有少量特征的系统来说效率较低。本研究提出一种张量补全算法，能够基于部分获取的数据集快速准确地预测缺失数据集，以辅助结构色设计。将结构色设计这一复杂的物理问题转化为连接几何参数和光谱数据的空间结构关系问题。该方法利用张量多线性数据分析有效地捕捉高阶数据中与几何参数和光谱数据相关的复杂关系。数值和实验结果表明，该算法在速度和准确性方面对各种结构、不同大小的数据集和不同材料都具有高可靠性，显著提高了设计效率。所提出的算法为涉及复杂物理系统的逆向设计问题提供了一种可行的解决方案，从而为光子器件的设计引入了一种新方法。此外，数值实验表明，带有菱形的十字形谐振器的结构色可以克服传统介电材料在蓝光波长区域观察到的高损耗问题，并提高结构的耐腐蚀性。通过这种结构，我们实现了宽色域和接近1的高窄反射光谱，理论分析进一步验证了菱形在光学领域具有巨大潜力。
