Deep Learning-Driven Extraction of Superluminescent Diodes Parameters

We present a deep learning-based method for the automatic extraction of physical parameters from optical spectra and power values of a chirped, tapered, dual-section quantum dot superluminescent diode. The neural network is able to estimate a set of parameters that are capable of reproducing the behavior of the target device with high accuracy.