Reliability-Aware Bayesian Optimization of 1310 nm PCSELs with FDTD Verification

2026-07-23

AI Summary

This study addresses the challenges in designing 1310 nm photonic crystal surface-emitting lasers (PCSELs), where minor geometric perturbations readily induce wavelength shifts, degraded beam quality, and instability in high-Q resonance fitting, while full-wave simulations remain computationally prohibitive. To overcome these issues, the authors propose a reliability-aware Bayesian optimization framework that integrates commercial FDTD simulations with surrogate modeling to jointly optimize wavelength, divergence angle, and resonant stability within an eight-dimensional local design space. A key innovation is the introduction of an effective Q-factor metric corrected by fitting error, which avoids overreliance on optimistic single-exponential decay fits. Experimental results demonstrate that, across three independent runs of 80 evaluations each, the method yields an average of 9.0 candidate designs satisfying all joint constraints, achieving effective Q-factors of 4.33×10⁶–7.76×10⁶ (a 60–108× improvement over baseline), wavelengths of 1308.23–1310.90 nm, and divergence angles near 0.84°, significantly outperforming differential evolution and Latin hypercube sampling.

Abstract

Near 1310 nm photonic-crystal surface-emitting lasers (PCSELs) are attractive narrow-beam sources for optical communication and sensing, but their final design refinement is costly. Small geometry changes simultaneously shift the band-edge resonance, cavity leakage, far-field divergence, and the numerical stability of a high-$Q$ decay fit, while every full-wave trial requires a time-domain simulation. We couple a commercial finite-difference time-domain solver to a reliability-aware Bayesian optimization (BO) loop over eight local design variables. Each completed simulation updates the surrogate used to choose the next geometry. Candidate ranking combines wavelength and beam-quality requirements with a reliability-adjusted metric $Q_{\mathrm{eff}}$ derived from the solver-reported relative fit-error estimate $dQ/Q$. Across three 80-evaluation runs from the same reference model, BO produced 5--15 candidates per run that passed the joint filter. Designs reconstructed from fresh model copies retained $Q_{\mathrm{eff}}=4.33\times10^6$--$7.76\times10^6$, a 60--108-fold increase over the baseline metric, at 1308.23--1310.90~nm with approximately $0.84^{\circ}$ divergence. Under equal budgets, BO gave the highest mean strict-filter yield (9.0 candidates), compared with differential evolution (7.0) and Latin-hypercube sampling (1.5), although the controls occasionally matched the peak $Q_{\mathrm{eff}}$. Field maps, resonance spectra, and local perturbations further identify an index-related wavelength handle and a hole-size-related leakage handle. The resulting FDTD budget produces a pool of wavelength-compatible, narrow-beam, and reproducible high-$Q$ PCSEL candidates without trusting a single optimistic decay fit.

Problem

• PCSELs
• Bayesian optimization
• FDTD
• high-Q
• reliability

Innovation

• Reliability-aware Bayesian optimization
• Photonic-crystal surface-emitting lasers (PCSELs)
• FDTD simulation
• High-Q cavity design
• Multi-objective optimization