Deep learning reconstruction algorithm for frequency-resolved optical gating

Abstract

In general, delay operation is the most time-consuming stage in frequency-resolved optical gating (FROG) technology, which limits the use of FROG for high-speed measurement of ultrashort laser pulses. In this work, we propose and demonstrate the reconstruction of ultrashort optical pulses by employing the sequence-to-sequence (Seq2Seq) model with attention, theoretically. To our knowledge, this is the first deep learning framework capable of accurately reconstructing ultrashort pulses using very partial spectrograms. The root mean squared error (RMSE) of the pulse amplitude reconstruction and phase reconstruction on the overall test dataset are 9.5 × 10−4 and 0.20, respectively. Compared with the classic FROG recovery algorithm based on two-dimensional phase retrieval algorithms, the use of our model can shorten the spectral measurement time to 1/8 of the original time or even less. Meanwhile, the time required for pulse reconstruction using our model is roughly 0.2 s. To our knowledge, the pulse reconstruction speed of our model exceeds all current iteration-based FROG recovery algorithms. We believe that this study can greatly facilitate the use of FROG for high-speed measurements of ultrashort pulses.