Measuring laser beams with a neural network.

Area of Science:

• Optics and Photonics
• Artificial Intelligence
• Image Processing

Background:

• Accurate measurement of laser beam parameters is crucial for various optical applications.
• Traditional methods for laser beam profiling can be complex and time-consuming.
• Developing automated and precise measurement techniques is an ongoing research area.

Purpose of the Study:

• To develop and evaluate a deep neural network (NN) for simultaneous detection and parameter measurement of laser beams.
• To assess the NN's performance using both simulated and experimental laser beam datasets.
• To demonstrate the NN's utility as a standalone or complementary tool for beam analysis.

Main Methods:

• A deep neural network (NN) architecture was designed for laser beam detection and parameter extraction.
• The NN was trained and validated using a dataset of simulated laser beams.
• The NN's performance was further evaluated on a dataset of experimental laser beams generated via a spatial light modulator.

Main Results:

• The NN achieved root mean square errors (RMSEs) below 3.4% for beam parameters after initial training on simulated data.
• Further training on experimental data reduced the RMSEs to below 1.1%.
• The NN demonstrated robust performance in measuring center coordinates, radii, and angular orientations.

Conclusions:

• The developed deep neural network provides an accurate and efficient method for laser beam parameter measurement.
• The NN can serve as a standalone solution or enhance existing beam profiling techniques by identifying regions of interest.
• This AI-driven approach offers a significant advancement in automated optical metrology.