School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China430074.
Nanoscale. 2019 Nov 21;11(45):21748-21758. doi: 10.1039/c9nr06127d.
Efficiently identifying optical structures with desired functionalities, referred to as inverse design, can dramatically accelerate the invention of new photonic devices, and this is especially useful in the design of large scale integrated photonic chips. Structural color with high-resolution, high-saturation, and low-loss holds great promise in image display, data storage and information security. However, the inverse design of structural color remains an open challenge, and this impedes practical application. Here, we propose an inverse design strategy for structural color using machine learning (ML) technologies. The supervised learning (SL) models are trained with the geometries and colors of dielectric arrays to capture accurate geometry-color relationships, and these are then applied to a reinforcement learning (RL) algorithm in order to find the optical structural geometries for the desired color. Our work succeeds in finding simple and accurate models to describe geometry-color relationships, which significantly improves the efficiency of the design. This strategy provides a systematic method to directly encode generic functionality into a set of structures and geometries, paving the way for the inverse design of functional photonic devices.
高效识别具有预期功能的光学结构，即所谓的反设计，可以极大地加速新型光子器件的发明，这在大规模集成光子芯片的设计中尤其有用。具有高分辨率、高饱和度和低损耗的结构色在图像显示、数据存储和信息安全方面具有广阔的应用前景。然而，结构色的反设计仍然是一个开放的挑战，这阻碍了其实际应用。在这里，我们提出了一种使用机器学习（ML）技术的结构色反设计策略。监督学习（SL）模型使用介电阵列的几何形状和颜色进行训练，以捕捉准确的几何形状-颜色关系，然后将这些关系应用于强化学习（RL）算法，以找到所需颜色的光学结构几何形状。我们的工作成功地找到了简单而准确的模型来描述几何形状-颜色关系，这显著提高了设计效率。该策略提供了一种将通用功能直接编码到一组结构和几何形状中的系统方法，为功能光子器件的反设计铺平了道路。
