Intelligent self calibration tool for adaptive few-mode fiber multiplexers using multiplane light conversion

Abstract

Space division multiplexing (SDM) is promising to enhance capacity limits of optical networks. Among implementation options, few-mode fibres (FMFs) offer high efficiency gains in terms of integratability and throughput per volume. However, to achieve low insertion loss and low crosstalk, the beam launching should match the fiber modes precisely. We propose an all-optical data-driven technique based on multiplane light conversion (MPLC) and neural networks (NNs). By using a phase-only spatial light modulator (SLM), spatially separated input beams are transformed independently to coaxial output modes. Compared to conventional offline calculation of SLM phase masks, we employ an intelligent two-stage approach that considers knowledge of the experimental environment significantly reducing misalignment. First, a single-layer NN called Model-NN learns the beam propagation through the setup and provides a digital twin of the apparatus. Second, another single-layer NN called Actor-NN controls the model. As a result, SLM phase masks are predicted and employed in the experiment to shape an input beam to a target output. We show results on a single-passage configuration with intensity-only shaping. We achieve a correlation between experiment and network prediction of 0.65. Using programmable optical elements, our method allows the implementation of aberration correction and distortion compensation techniques, which enables secure high-capacity long-reach FMF-based communication systems by adaptive mode multiplexing devices.