The parameters group of the distributed feedback (DFB) laser equivalent circuit model based on the single-mode rate equations is the key to precisely presenting DFB response characteristics, so a novel optimization solution based on the response surface methodology (RSM) is proposed to rapidly select the optimized parameters by the multi-objective algorithm. The RSM model is designed to match the DFB laser characteristics related to the direct-current and small-signal frequency response, and non-dominated sorting genetic algorithm-II (NSGA-II) attributes to elevating the RSM model optimizing to screen out an optimal set of parameters by Pareto sorting. To further verify the accuracy of the model, the resonant frequency (fr) and the threshold current (Ith) are considered the objective optimization variables to set the target values as 18 GHz and 11.5 mA. The single-objective and multi-objective optimization are analyzed and compared to each other, and the optimized results have shown good agreement with predicted values, such as lower Ith in the multi-objective optimization while close fr in both cases. It has been demonstrated that optimization makes it possible not only to exploit the potential of existing DFB lasers but also to provide guidance for the inverse design of laser.

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 rd , 5 th , and 8 th 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.

The shaping current technology can efficiently and low-costly suppress the relaxation oscillations (ROs) of the direct modulation semiconductor laser (DML) for the high-performance optic system. The parameter selection is the key problem to precisely constructing the injection current form to obtain the desired output waveform. A novel framework based on convolutional neural network (CNN) is proposed to predict the shaped current parameters avoiding the time-consuming and computationally complex problems with analytical solutions. In the network training, batch and min-max normalizations are adopted to optimize neural networks, which aim to accelerate the convergence and improve their approximation ability. The trained inverse CNN named by feeding into the desired data samples from DML output waveform is used to achieve parameter selection for constructing the injection current. Also, the trained forward CNN would verify the validity of selected parameters responding to the output waveform, and get the unique corresponding relationship between them. Simulation results own high agreement with the theoretical values and show that the CNN models provide a powerful tool to select parameters of shaped current with accurate and fast capabilities.