Modeling extended L-band fiber amplifiers using neural networks trained on experimental data

Abstract

Producing high performance amplifiers requires accurate numerical models. As the optimization space is large, computationally efficient models are of great value. Parameter-based models for L-band amplifiers have accuracy limited by difficulty in estimating the Giles-parameter. The use a neural network model can avoid parametrization. We exploit a rich, experimentally captured training set to achieve a high accuracy neural network model. Our approach creates independent models for gain and noise figure. We examine both core and cladding pumping methods, again with independent models for each. The neural networks outperform parameter-based models with higher accuracy (variance of error reduced by 50%) and extremely fast simulation times (400 times faster), greatly facilitating amplifier design. As an example application, we design an amplifier to optimize optical signal-to-noise ratio by exhaustive search with our fast neural network models.