Open AccessIntelligent gain flattening in wavelength and space domain for FMF Raman amplification by machine learning based inverse design
Author(s) -
Yufeng Chen,
Jiangbing Du,
Yu-Ting Huang,
Ke Xu,
Zuyuan He
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.387820
Subject(s) - flatness (cosmology) , flattening , optics , raman amplification , wavelength , amplified spontaneous emission , fiber laser , inverse , laser , materials science , computer science , physics , optical amplifier , mathematics , cosmology , quantum mechanics , composite material , geometry
We propose a machine learning based approach to design few-mode DRAs by using neural networks to optimize the pump wavelengths, powers and mode content in order to obtain flat gain spectrum with low mode-dependent gain (MDG). Based on the proposed intelligent inverse design method, amplification optimization for the random fiber laser based two-mode DRA can be achieved with gain flatness of 1.0 dB and MDG of 0.6 dB at 14.5 dB on-off gain level. For backward pumping four-mode DRA, gain flatness of 0.46 dB and MDG of 0.3 dB can be achieved at 12.5 dB on-off gain.