Abstract

The multimode waveguide is essential for future mode-division-multiplexing optical interconnection systems to further improve data capacity. However, the complex waveguide design process based on numerical methods is time consuming and requires a lot of computational effort. By using machine learning trained models, one can find the rules from the observed data (sample) and use the learned rules (model) to predict the unknown or unobservable data quickly. In this paper, to accelerate the multimode waveguide design, several regression models consisting of fully connected and long short-term memory layers are employed to predict the effective refractive indices from the fundamental mode to fourth-order TE mode with various waveguide geometries. For air cladding waveguides, the percentages of eligible data whose prediction errors are less than ${{10}^{- 3}}$ of different modes are 89.95%, 88.10%, 82.29%, 75.83%, and 71.19%. For ${{\rm SiO}_2}$ cladding ones, they are 95.40%, 92.81%, 90.90%, 81.99%, and 86.39%. Based on these models, two kinds of mode multiplexers are designed, and coupling efficiency of higher than 85% is achieved.

© 2022 Optica Publishing Group

Inverse design of dispersion for photonic devices based on LSTM and gradient-free optimization algorithms hybridization

Tianhang Yao, Tianye Huang, Bin Yan, Mingfeng Ge, Jie Yin, Chuyu Peng, Lu Li, Wufeng Sun, and Perry Ping Shum
J. Opt. Soc. Am. B 40(6) 1525-1532 (2023)

Few-mode fiber design for multiple-input-multiple-output-less mode division multiplexing by machine learning

Yudan Cheng, Yinghao Guo, Min Cao, Youchao Jiang, Wenhua Ren, and Guobin Ren
J. Opt. Soc. Am. B 39(9) 2421-2428 (2022)

Deep-learning-assisted designing chiral terahertz metamaterials with asymmetric transmission properties

Feng Gao, Zhen Zhang, Yafei Xu, Liuyang Zhang, Ruqiang Yan, and Xuefeng Chen
J. Opt. Soc. Am. B 39(6) 1511-1519 (2022)

Ensemble learning: a bidirectional framework for designing data-driven THz composite metamaterials

Yue Wang, Yongqiang Zhu, Zijian Cui, Haoqing Jiang, Kuang Zhang, and Xuan Wang
J. Opt. Soc. Am. B 39(3) 835-842 (2022)

Deep learning for the design of 3D chiral plasmonic metasurfaces

Xianglai Liao, Lili Gui, Zhenming Yu, Tian Zhang, and Kun Xu
Opt. Mater. Express 12(2) 758-771 (2022)