Hermite-Gaussian Mode Detection via Convolution Neural Networks

Abstract

Hermite-Gaussian (HG) laser modes are a complete set of solutions to the free-space paraxial wave equation in Cartesian coordinates and represent a close approximation to physically-realizable laser cavity modes. Additionally, HG modes can be mode-multiplexed to significantly increase the information capacity of optical communication systems due to their orthogonality. Since, both cavity tuning and optical communication applications benefit from a machine vision determination of HG modes, convolution neural networks were implemented to detect the lowest twenty-one unique HG modes with an accuracy greater than 99%. As the effectiveness of a CNN is dependent on the diversity of its training data, extensive simulated and experimental datasets were created for training, validation and testing.

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• How transferable are features in deep neural networks?
• The Marginal Value of Adaptive Gradient Methods in Machine Learning
• Communication with spatially modulated Light through turbulent Air across Vienna
• Twisted Light Transmission over 143 kilometers
• Deep Image: Scaling up Image Recognition
• On the use of deep neural networks in optical communications
• The Resilience of Hermite- and Laguerre-Gaussian Modes in Turbulence
• Real-time determination of laser beam quality by modal decomposition

Cited by in corpus (4)

• Efficient sorting of orbital-angular-momentum states with large topological charges and their unknown superpositions via machine learning
• Hyperparameter tuning of optical neural network classifiers for high-order gaussian beams
• Atom Cloud Detection Using a Deep Neural Network
• Measuring Laser Beams with a Neural Network