Spatial optical mode decomposition using deep learning

Mumtaz Sheikh

doi:10.1117/12.2568424

20 August 2020 Spatial optical mode decomposition using deep learning

Mumtaz Sheikh

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110M (2020) https://doi.org/10.1117/12.2568424
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

We demonstrate using a convolutional neural network (CNN) architecture such as Resnet20 how to perform complete Laguerre-Gauss (LG) decomposition of an unknown, incoming laser beam using only intensity images. For our proof-ofconcept simulations, we use random super-positions of first 10 azimuthal LG modes with l-values from 0 to 9. For these random super-positions, both the amplitudes and phase values are random. Random white noise is added to the intensity images of these simulated fields. 80,000 such training images are used to train our Resnet20 CNN while another 20,000 images are in the test set. Prediction results on the test set show a correlation value of 98.75% showing the efficacy of using a CNN to perform spatial mode decomposition.

Conference Presentation

Citation Download Citation

Mumtaz Sheikh "Spatial optical mode decomposition using deep learning", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110M (20 August 2020); https://doi.org/10.1117/12.2568424

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