基于深度学习的多模波导有效折射率预测

2021 19th International Conference on Optical Communications and Networks (ICOCN) Pub Date : 2021-08-23 DOI:10.1109/ICOCN53177.2021.9563662

Tianhang Yao, Tianye Huang, Yuan Xie, Zhichao Wu, Dapeng Luo, Zhuo Cheng, P. Ping

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引用次数: 0

摘要

为了加速多模波导的设计，采用了几种回归模型来预测不同波导几何参数下从基模到四阶TE模的有效折射率(neff)。在空气包覆数据集上，不同模式下预测误差小于10-3的合格数据比例分别为89.95%、88.10%、82.29%、75.83%、71.19%。在SiO2包覆数据集上，它们分别为95.40%、92.81%、90.90%、81.99%、86.39%。本研究为基于机器学习的光波导结构设计与优化提供了指导。

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Predicting Effective Refractive Indices of Multimode Waveguide via Deep Learning

In order to accelerate the multimode waveguide design, several regression models are employed to predict the effective refractive indices (neff) from fundamental mode to fourth-order TE mode with various waveguide geometric parameters. On dataset with air cladding, the percent of eligible data whose prediction error is less than 10–3 of different modes are respectively 89.95%, 88.10%, 82.29%, 75.83%, 71.19%. And on dataset with SiO2 cladding, they are 95.40%, 92.81 %, 90.90%, 81.99%, 86.39%. This study guides the structural design and optimization of optical waveguides based on machine learning.

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2021 19th International Conference on Optical Communications and Networks (ICOCN)

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