AIML-Based Accounting of Physical Constraints: Transformer Optimization for Biomedical PhC Systems
Annotatsiya
Photonic-crystal (PhC) devices form the backbone of many label-free biomedical sensors due to sharp resonance features and high sensitivity to refractive-index changes. High-fidelity electromagnetic solvers accurately predict device responses but remain computationally expensive for design exploration and real-time inference in clinical settings. This paper presents a physics-aware transformer surrogate that incorporates Maxwell constraints and fabrication tolerances directly into the training objective to deliver fast, accurate spectral predictions and physics-consistent inverse design. The surrogate maps geometric and material parameterizations to spectral observables (resonant frequency, Q-factor, transmission spectra) using a tokenized representation of device geometry and frequency channels, augmented by a PDE-residual head enforcing the frequency-domain Maxwell eigenproblem. Training leverages a hybrid dataset of finite-difference time-domain (FDTD) simulations and limited experimental measurements for fine-tuning. Empirically, the surrogate achieves parity-level spectral accuracy (spectral RMSE ≈ 0.03, resonant MAE ≈ 0.4 nm, R^2≈ 0.98), reduces mean PDE residuals substantially versus data-only baselines, and delivers ≈ 100 × speedups ( ≈ 8 ms/sample on GPU) for inverse-design loops. An inverse-design vignette shows a ≈15% improvement in limit-of-detection, and robustness tests indicate markedly lower sensitivity to ±3% fabrication jitter. This work release code, dataset manifests, and checkpoints to support reproducibility; open challenges include dataset shift, adaptation to novel fabrication modalities, and hardware-constrained deployment.