Spatial Mode Classification Using Machine Learning Regression
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Solution Overview
Problem
Current methods for classifying higher-order spatial modes in multimode optical fibers are hindered by the need for indirect measurements using unconventional optical devices, alignment dependence, high costs, and the requirement for extensive experimentally generated training examples, which limits their efficacy and accuracy, especially when dealing with complex electric field amplitudes having multiple polarization components.
Innovation Solution
A machine learning-based approach utilizing a convolutional neural network trained with numerically calculated intensity images of Hermite-Gaussian and Laguerre-Gaussian modes allows for the decomposition of light beams into their constituent spatial modes, even when relative phases are unknown, and enables classification without relying on indirect measurements or alignment-sensitive techniques, using a conventional camera and polarization optical elements to separate and classify multiple polarization components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect measurement using interferometry or holographic techniques via unconventional optical devices is used, then classification of higher-order spatial modes can be achieved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses a spatial light modulator to generate holographic copies of the complex amplitude patterns of higher-order spatial modes. By comparing the measured beam pattern against these pre-generated holographic copies, the system achieves accurate classification without requiring complex interferometric setups or custom optical elements for each mode.
Solution Approach 2:
The patent introduces a spatial light modulator as an intermediary device that can dynamically generate and switch between different spatial mode patterns. This intermediary enables flexible mode classification by programmatically creating reference patterns rather than requiring direct physical measurement setups for each mode type.
2Measurement precision
If unconventional optical devices such as spatial light modulators or custom optical elements are used, then higher-order spatial modes can be classified, but cost and fabrication requirements increase
Solution Approach 1:
The patent employs a spatial light modulator as a universal device that can generate multiple different spatial mode patterns (Hermite-Gaussian, Laguerre-Gaussian, and other higher-order modes) through programming, rather than requiring separate custom optical elements for each mode type. This multi-functional approach reduces fabrication complexity and cost.
Solution Approach 2:
The patent changes the operational parameters of the spatial light modulator (displayed hologram patterns, phase profiles, amplitude distributions) to generate different spatial mode references dynamically. This parametric control eliminates the need for physical fabrication of custom optical elements for each mode classification scenario.
3Measurement precision
If canonical classification methods using interferometry are used, then spatial mode classification can be performed, but dependence on beam alignment, size, and wave front increases
Solution Approach 1:
The patent performs preliminary generation and storage of holographic reference patterns for various spatial modes under different conditions (alignments, sizes, wavefronts) during system setup. This preliminary action creates a comprehensive reference library that enables robust classification without requiring precise real-time alignment during operation.
Solution Approach 2:
The patent uses a spatial light modulator to dynamically adjust and match the reference holographic patterns to the actual beam characteristics during classification. This dynamic adaptation allows the system to compensate for variations in alignment, size, and wavefront, reducing operational sensitivity to these parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method accurately decomposes light beams into Hermite-Gaussian and Laguerre-Gaussian modes, reducing dependency on alignment, size, and wave front, eliminating the need for costly unconventional devices and extensive training examples, and allows for separate classification of different linear combinations of higher-order spatial modes in each polarization component.
Implementation Method 1
an image capture device captures image data of the transverse, spatial dependencies of the intensities of the of more than one of orthogonal polarization components of a light beam
Implementation Method 2
using a machine learning based classifier, a processor classifies the image data of the transverse, spatial dependencies of the intensities of the more than one polarization components
Data Source
AI summary
Aspects of the present disclosure describe systems, methods, and structures for the machine learning based regression of complex coefficients of a linear combination of spatial modes from a multimode optical fiber.