Experimental beam combining stabilization using machine learning trained while phases drift.

Area of Science:

• Optics and Photonics
• Laser Technology
• Control Systems Engineering

Background:

• Coherent beam combination (CBC) is crucial for high-power laser systems.
• Maintaining phase stability in multi-beam systems is a significant challenge due to environmental factors and component drift.
• Diffractive optical elements offer compact solutions for beam combination but require precise phase control.

Purpose of the Study:

• To develop and validate a learning algorithm for active phase control in a diffractive coherent beam combiner.
• To assess the stability and efficiency of the beam combination process under dynamic phase conditions.
• To demonstrate the effectiveness of a differential mapping technique for real-time phase correction.

Main Methods:

• An 8-beam diffractive coherent beam combiner was utilized.
• A learning algorithm was trained to control optical phases in real-time.
• A differential mapping technique was employed to monitor and correct phase drifts.
• Output power and efficiency were measured to evaluate performance.

Main Results:

• The learning algorithm successfully stabilized the optical phases despite drift.
• Combined output power was maintained with a stability of 0.4%.
• The system achieved 95% of the theoretical maximum efficiency, limited by the diffractive element's characteristics.

Conclusions:

• The developed learning algorithm provides effective phase control for diffractive coherent beam combiners.
• This approach significantly enhances the stability and efficiency of high-power laser output.
• The technique shows promise for applications requiring robust and high-performance coherent beam combination.