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

Abstract

Rate equations and numerical simulations relying on complex mathematical and physical principles are typically used to model directly modulated lasers (DMLs) but have difficulty simulating dynamic DML behavior in real-time under varying conditions due to their high complexity. Here, we introduce a data-driven deep learning method to model DMLs, aiming to achieve high accuracy with reduced computational complexity. This approach employs bidirectional long short-term memory (BiLSTM) enhanced by advanced feature recalibration and nonlinear fitting techniques. The result is compared with LSTM, standard BiLSTM, and recurrent neural network (RNN) architectures. The proposed model obtains the best results for the evaluated metrics. The satisfactory output waveforms and acceptable spectra indicate that the proposed model offers an accurate and real-time method to model DMLs.