VCSEL-based PAM-4 transmission system emulator: a data-driven deep-learning perspective

We demonstrate a data-driven framework for emulating high-speed VCSEL-based four-level pulse amplitude modulation (PAM-4) optical interconnects using bidirectional long short-term memory (Bi-LSTM) networks. Unlike conventional rate-equation models, which are computationally intensive and often require difficult parameter tuning, our approach utilizes experimental waveforms to learn the end-to-end system dynamics. By employing transfer learning and weight interpolation, we extend the model to new operating regimes with a 20-fold reduction in computation time compared to independent training, while maintaining a normalized mean squared error below 0.04. This emulator provides a rapid, accurate tool for the design and optimization of short-reach optical links.