Intelligent Mode-Locked Single-Cavity Dual-Comb Laser Utilizing Time-Stretch Dispersive Fourier Transform Spectroscopy

TL;DR

This paper presents a novel intelligent method for stable dual-comb mode locking in a single-cavity laser using a combination of CNN-Transformer classification, evolutionary algorithms, and dispersive Fourier transform spectroscopy, enabling rapid and reliable dual-comb generation.

Contribution

It introduces an integrated approach combining machine learning, evolutionary algorithms, and dispersive Fourier transform to achieve and maintain dual-comb mode locking in a single-cavity laser.

Findings

Achieves dual-comb mode locking within 2 seconds.

Uses simulated spectra for optimization, avoiding experimental data dependence.

Ensures long-term stability with a collision-based algorithm.

Abstract

As dual combs play a significant role in numerous high-precision measurements, their efficient generation has been widely researched. Although the single-cavity dual-comb generation can avoid the complex active stabilization methods, achieving and maintaining stable dual-comb mode locking within a single cavity remains a critical challenge. To break through this constraint, a two-part evaluation criterion containing a fitness function and a CNN-Transformer network is employed to achieve mode locking and classify the dual-comb mode-locked state. Simulated time-stretch dispersive Fourier transform (DFT) spectra are used as datasets, which simplifies the optimization process and does not rely on specific experimental data. A developed evolutionary algorithm (EA) for paddle-based motorized polarization controllers (MPCs) is proposed, enabling the intelligent attainment of dual-comb mode-locked states. A real-time library stores fitness and MPC angles, facilitating mode-locked state achievement within 2 seconds. Finally, long term running of dual-comb mode locking is ensured by a random collision algorithm utilizing an evaluation criterion of weak soliton peaks.