Yan Ruoqin, Wang Tao, Jiang Xiaoyun, Huang Xing, Wang Lu, Yue Xinzhao, Wang Huimin, Wang Yuandong
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.
Nanotechnology. 2021 May 24;32(33). doi: 10.1088/1361-6528/abff8d.
The development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. How to efficiently design these devices is an active area of research. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. However, there is limited research on the inverse design for modeling and learning the sequence characteristics of a spectrum. In this work, we propose a deep learning method based on an improved recurrent neural network to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction. A key feature of the network is that the memory or feedback loops it comprises allow it to effectively recognize time series data. In the context of nanorods hyperbolic metamaterials, we demonstrated the high consistency between the target spectrum and the predicted spectrum, and the network learned the deep physical relationship concerning the structural parameter changes reflected on the spectrum. The effectiveness of our approach is also tested by user-drawn spectra. Moreover, the proposed model is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error. The prediction model may be helpful to predict data beyond the detection limit. We propose this versatile method as an effective and accurate alternative to the application of ANNs in nanophotonics, paving way for fast and accurate design of desired devices.
纳米光子器件的发展为在纳米尺度上操纵光提供了一种革命性的手段。如何高效设计这些器件是一个活跃的研究领域。最近，人工神经网络（ANN）在纳米光子器件的逆向设计中展现出强大能力。然而，针对光谱序列特征建模与学习的逆向设计研究有限。在这项工作中，我们提出一种基于改进循环神经网络的深度学习方法，以提取光谱的序列特征并实现逆向设计和光谱预测。该网络的一个关键特性是其包含的记忆或反馈回路使其能够有效识别时间序列数据。在纳米棒双曲超材料的背景下，我们证明了目标光谱与预测光谱之间的高度一致性，并且该网络学习到了与光谱上反映的结构参数变化相关的深层物理关系。我们的方法的有效性也通过用户绘制的光谱进行了测试。此外，所提出的模型能够基于已知光谱预测未知光谱，平均相对误差仅为0.32%。该预测模型可能有助于预测超出检测极限的数据。我们提出这种通用方法，作为ANN在纳米光子学应用中的一种有效且准确的替代方案，为快速准确地设计所需器件铺平道路。
