Suppressing DFB Lasers Relaxation Oscillations by High-Order Continuous Shaping Current With CNN Prediction

Abstract

A technique of continuous shaping current waveform to suppress relaxation oscillations (ROs) of distributed feedback (DFB) laser for a high-performance optic system is demonstrated. To effectively suppress ROs, expressions for the shaping current waveform are theoretically derived based on the rate equations and different polynomials for the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> , 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , and 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order Fourier basis functions are introduced. The convolutional neural network (CNN) is employed to predict the multi-parameter values that determine the results of the shaping input current, which exempt from the difficult and time-consuming process of parameter selection. Prior to training, preprocessing of the data obtained from DFB laser forward simulation using min-max normalization aims to improve the training efficiency of the CNN. The shaping current signals obtained from the CNN predicted parameters are put into the equivalent circuit model for the DFB laser to verify the effectiveness of the shaping current technique and CNN parameter optimization. Afterwards, the shaping current waveform is verified in a time division multiplex passive optical network (TDM-PON) utilizing the DFB laser model as a directly modulated source achieving remarkable performance with low cost. The results show that the high-order continuous shaping current modulated technique can successfully suppress the ROs and enhance the performance of the optic system.