Machine learning aided inverse design for few-mode fiber weak-coupling optimization
Zhiqin He, Jiangbing Du, Xinyi Chen, Weihong Shen, Yuting Huang, Chang Wang, Ke Xu, Zuyuan He
Abstract
Few-mode fiber (FMF) supporting many modes with weak-coupling is highly desired in mode division multiplexing (MDM) systems. The multi-parameter design of FMF becomes comparably difficult, inaccurate and time-consuming when it comes for complex fiber structures and many high order modes. In this work, we demonstrate a machine learning method using neural network to inversely design the desired FMF based on multiple-ring structure. By using the minimum index difference between adjacent modes as the weak-coupling optimization aim, we realize the inverse design of 4-ring step-index FMFs for supporting 4, 6 and 10 -mode operation, and 6-ring step-index FMF for supporting 20-mode operation. This method provides high-accuracy, high-efficiency and low-complexity for fast and reusable design of optical fibers, including particularly weak-coupling FMF in this work. It can be widely extended to a lot of fibers and has great potential for instantaneous applications in the optical fiber industry.