Intelligent polarization manipulation in ultrafast fiber lasers: A bibliometric perspective on hardware and algorithmic co-development

Intelligent polarization control serves as a critical prerequisite for overcoming the environmental instability of ultrafast fiber lasers, unlocking their potential for high-precision applications outside controlled laboratory settings. This paper systematically reviews the evolution of this field, addressing the challenges of stabilizing pulse dynamics against external perturbations. Through a bibliometric analysis, algorithmic strategies are categorized into traversal, optimization, and machine learning approaches, alongside a detailed elucidation of the physical mechanisms governing stress-induced, liquid-crystal, electro-optic, and magneto-optic controllers. A distinct technological paradigm shift is identified toward the integration of high-speed electro-optic devices with machine learning, a synergy capable of achieving mode-locking and recovery on millisecond timescales. Looking forward, it is proposed that the realization of fully autonomous and robust ultrafast light sources will necessitate a transition toward physics-driven artificial intelligence and field-programmable gate array (FPGA)-based high-speed hardware architectures. In the future, advances in these areas will provide more possibilities for achieving unattended self-starting, self-recovery after loss of lock, and dynamic adaptive control of pulse parameters for mode-locked lasers in extreme environments.