Machine-Learning-Assisted Dual Harmonic Generation FROG for Enhanced Ultrafast Pulse Recovery

Ultrafast pulse characterisation is crucial for studying processes that occur at femtosecond timescales and below. Because of this, various methods have been developed to recover a pulse's electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being the most common. However, this approach is computationally expensive and suffers from limitations in terms of robustness and reliability. In this regard, recent publications have demonstrated that applying machine learning towards ultrafast pulse recovery can alleviate these issues, providing more accurate retrievals. Inspired by these works, we propose an encoder–decoder scheme for a FROG system which exploits dual harmonic generation in low-index thin films. Specifically, we demonstrate enhanced reliability and accuracy of ultrafast pulse recovery when compared to machine learning approaches using second or third harmonic signals independently. As the amount of information used to train each neural network is kept constant, this study demonstrates and benchmarks the technological advantages of contextual information analysis involving multiple nonlinear processes.