Accurate deep learning based method for real-time directly modulated laser modeling.

Area of Science:

• Optoelectronics
• Computational Physics
• Machine Learning

Background:

• Directly modulated lasers (DMLs) are crucial in optical communications.
• Traditional modeling methods (rate equations, numerical simulations) are computationally intensive and struggle with real-time dynamic behavior.
• High complexity limits the simulation of DMLs under varying operational conditions.

Purpose of the Study:

• To develop a computationally efficient and accurate method for modeling directly modulated lasers (DMLs).
• To leverage data-driven deep learning techniques for simulating dynamic DML behavior.
• To outperform existing deep learning architectures in DML modeling accuracy and speed.

Main Methods:

• Implementation of a data-driven deep learning model utilizing bidirectional long short-term memory (BiLSTM).
• Enhancement of the BiLSTM model with advanced feature recalibration and nonlinear fitting techniques.
• Comparative analysis against standard LSTM, basic BiLSTM, and recurrent neural network (RNN) models.

Main Results:

• The proposed BiLSTM model achieved superior performance across evaluated metrics compared to LSTM, standard BiLSTM, and RNN.
• The model demonstrated satisfactory output waveforms, indicating accurate signal prediction.
• Acceptable spectral characteristics were reproduced, confirming the model's ability to capture essential laser dynamics.

Conclusions:

• The developed deep learning approach offers an accurate and real-time solution for modeling directly modulated lasers (DMLs).
• This method significantly reduces computational complexity, enabling faster simulations.
• The BiLSTM-based model presents a viable alternative to traditional, complex simulation techniques for DMLs.