Machine-learning-assisted loss prediction and analysis of photonic lantern MUX/DEMUX for SDM systems

Photonic lanterns (PLs) are pivotal passive components for mode-division multiplexing (MDM) in space-division multiplexed (SDM) optical interconnects, enabling low-loss, adiabatic coupling between multiple single-mode and multimode fiber. However, their design relies on computationally intensive full-wave electromagnetic solvers, such as the finite-element method (FEM), beam propagation method (BPM), and finite-difference time-domain (FDTD), which hinder efficient multi-objective optimization across high-dimensional parameter spaces. To overcome this bottleneck, we propose an XGBoost-based supervised surrogate modeling framework for simultaneous prediction of insertion loss (IL), mode-dependent loss (MDL), and polarization-dependent loss (PDL), trained on a physics-validated 2,000-sample FEM dataset spanning seven multicore geometries across the full C-band (1530–1590 nm). The surrogate achieves R²> 0.98 , RMSE < 0.13\,dB , and MAE < 0.075\,dB across all three targets, reducing per-design evaluation time from ~ 6.8\,h (FEM/BPM) to <1\,ms —a speedup of ~ 2.45 × 10⁷ —while maintaining sub- 0.1\,dB spectral stability across the full C-band. This capability enables rapid parametric sweeps of PL configurations in under 10\,s , supporting automated multi-objective optimization for MIMO-free MDM intra-data-center links at link lengths below 2\,km .