Accurate, robust, and fast mode decomposition for few-mode optical fiber with deep neural network

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We introduce deep learning technique to perform robust mode decomposition (MD) for few-mode optical fiber. Our goal is to learn a robust, fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including both of the modal amplitudes and phases. Taking a few-mode fiber which supports 3 linearly polarized modes into consideration, simulated near-field beam profiles with known mode coefficient labels are generated and fed into the convolutional neural network (CNN) to carry out the training procedure. Further, saturated patterns are added into the training samples to increase the robustness. When the network gets convergence, ordinary and saturated beam patterns are both utilized to perform MD with pre-trained CNN. The average correlation value of the input and reconstructed patterns can reach as high as 0.9994 and 0.9959 respectively for two cases. The consuming time of MD for one beam pattern is about 10ms. The results have shown that deep learning techniques highly favors the accurate, robust and fast MD for few-mode fiber.