Optimization and realignment of OAM mode excitation in ring-core optical fibers using machine learning

Area of Science:

• Optics and Photonics
• Quantum Information Science

Background:

• Light beams with orbital angular momentum (OAM) have diverse applications.
• Exciting OAM modes with high purity and low loss is challenging.
• Mechanical drift degrades OAM mode purity over time, limiting applications.

Purpose of the Study:

• To develop a rapid and automated method for exciting OAM modes.
• To optimize OAM mode purity and reduce excitation loss.
• To compensate for mechanical drift and maintain OAM purity over extended durations.

Main Methods:

• Combined an iterative wavefront matching approach with a genetic algorithm.
• Implemented systematic computational realignment for drift compensation.
• Experimental validation of the automated excitation and stabilization technique.

Main Results:

• Achieved rapid and automated excitation of OAM modes.
• Demonstrated optimized purity and reduced loss in OAM mode excitation.
• Maintained high OAM purity for over 24 hours, compensating for drift.

Conclusions:

• The developed method enables stable and high-purity OAM mode generation.
• Automated drift compensation overcomes a key limitation for practical OAM applications.
• Paves the way for robust deployment of OAM beams in various optical systems.