Hermite-Gaussian mode detection via convolution neural networks.

Area of Science:

• Optics and Photonics
• Machine Learning
• Optical Communications

Background:

• Hermite-Gaussian (HG) modes are fundamental solutions to the paraxial wave equation in Cartesian coordinates.
• HG modes are orthogonal, enabling mode-multiplexing to enhance optical communication capacity.
• Accurate identification of HG modes is vital for laser cavity tuning and optical communication systems.

Purpose of the Study:

• To develop a machine vision system for accurate detection of Hermite-Gaussian (HG) laser modes.
• To implement convolution neural networks (CNNs) for identifying the lowest 21 unique HG modes.

Main Methods:

• Creation of extensive simulated and experimental datasets for training, validation, and testing CNNs.
• Application of convolution neural networks for the classification and detection of HG laser modes.
• Ensuring diversity in training data to enhance CNN performance and generalization.

Main Results:

• Achieved a detection accuracy exceeding 99% for the lowest 21 unique HG modes.
• Demonstrated the effectiveness of CNNs in identifying HG laser modes from complex datasets.
• Validated the approach using both simulated and experimentally generated data.

Conclusions:

• CNNs provide a highly accurate and efficient method for machine vision determination of HG laser modes.
• The developed method supports advancements in laser cavity tuning and high-capacity optical communication systems.
• Diverse training data is essential for robust performance of CNNs in optical mode analysis.