Analyzing OAM mode purity in optical fibers with CNN-based deep learning

Abstract

Inspired by recent rapid deep learning development, we present a convolutional-neural-network (CNN)-based algorithm to predict orbital angular momentum (OAM) mode purity in optical fibers using far-field patterns. It is found that this image-processing-based technique has an excellent ability in predicting the OAM mode purity, potentially eliminating the need of using bulk optic devices to project light into different polarization states in traditional methods. The excellent performance of our algorithm can be characterized by a prediction accuracy of 99.8% and correlation coefficient of 0.99994. Furthermore, the robustness of this technique against different sizes of testing sets and different phases between different fiber modes is also verified. Hence, such a technique has a great potential in simplifying the measuring process of OAM purity.