Parameter Selection of Direct Modulation Semiconductor Laser for Shaping Current Based on Convolutional Neural Network

• Published in: IEEE Photonics Journal
• Main Authors: Qing-An Ding, Huixin Liu, Xiaojuan Wang, Gaoyang Zhu, Liuge Du, Xudong Cheng, Li Zheng, Junkai Li, Qunying Yang
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2022
• Subjects: Semiconductor lasers; relaxation oscillations; parameter selection; convolutional neural network; Applied optics. Photonics; TA1501-1820; Optics. Light; QC350-467; DML
• Online Access: https://doi.org/10.1109/JPHOT.2022.3221118; https://doaj.org/article/77fb05fe80da4ae2b78a111966c56fce

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.