The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams

Area of Science:

• Optics and Photonics
• Machine Learning Applications
• Instrumentation

Background:

• Manual alignment of laser beams into optical fibers is time-consuming and requires expertise.
• Suboptimal alignment reduces signal transmission efficiency and can impact experimental reproducibility.
• Automated solutions are needed to enhance precision and reduce manual labor.

Purpose of the Study:

• To develop and validate a novel automated device for beam alignment optimization.
• To improve the efficiency and accuracy of coupling laser beams into single-mode optical fibers.
• To demonstrate the utility of open-source machine learning algorithms in experimental setups.

Main Methods:

• A custom-built device integrating a Raspberry Pi, stepper motors, optomechanics, and electronics was employed.
• The open-source machine learning algorithm M-LOOP was utilized for automated optimization.
• Schematic drawings of the custom hardware and diagnostic techniques for performance evaluation were provided.

Main Results:

• The automated beam alignment device successfully improved laser beam coupling into a single-mode optical fiber.
• Manual alignment was outperformed, with the automated system achieving alignment in approximately 20 minutes per iteration.
• Example data demonstrating the device's performance in a measurement scenario were presented.

Conclusions:

• The developed automated beam alignment system offers a practical and efficient solution for optical experiments.
• Integration of Raspberry Pi and M-LOOP provides a cost-effective and accessible platform for automated alignment.
• This technology has the potential to streamline experimental workflows and improve data quality in photonics research.