| Autor: |
Qing-An Ding, Huixin Liu, Xiaojuan Wang, Gaoyang Zhu, Liuge Du, Xudong Cheng, Li Zheng, Junkai Li, Qunying Yang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Photonics Journal, Vol 14, Iss 6, Pp 1-8 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2022.3221118 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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