Efficient Multi-Objective Parameter Extraction for DFB Lasers Based on the Deep Learning

Abstract

Parameters used to calculate the operating characteristics of distributed feedback (DFB) lasers have to be chosen properly to ensure a perfect match between the calculated and measured results. We propose a multi-objective parameter extraction method based on the deep neural network (DNN), that can not only efficiently and accurately extract the realistic parameters of DFB lasers but also resolve coupling issues among parameters. Two forward DNN models are trained using traveling wave model (TWM) and transfer matrix method (TMM), respectively, to predict the light-current (L-I) characteristics, amplitude modulation (AM) response, and spectrum of DFB lasers. After fabricating and measuring lasers, the extracted parameters are iteratively optimized against the experimentally measured results utilizing the well-performing forward models and differential evolution algorithm. Through Pareto sorting, an optimal parameter set is extracted, which can accurately reproduce all target characteristics (the LI curve, AM response, and spectrum) at the same time. The proposed method greatly reduces the computational complexity and avoids the time-consuming situation of traditional TWM and TMM calculations. Using the extracted parameters, the large-signal modulation is theoretically calculated. And it fits very well with the measured results, which verifies the practicality and reliability of the proposed method.