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

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.