Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout
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Abstract (arXiv)
Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision depends on localized output-port readouts. This risk is acute in propagation-dominated MMI splitters and couplers, where port power, splitting, phase, and coupling are determined by accumulated modal interference and output-window aggregation rather than by average field similarity alone. We study this field-to-design mismatch through a Field/Mediator/Readout view that separates dense complex-field error from propagation-profile and output-window errors before port aggregation. To align the surrogate with this chain, we propose PaNO, a propagation-aligned neural operator that keeps the full-field prediction interface while organizing latent states around local boundary structure, transverse modal content, axial propagation, and cross-mode interaction. We also evaluate PaNO-R2, an output-aware feedback variant for residual field components near the port region. On a 15-wavelength tunable 3×3 MMI benchmark with 4608 held-out fields, PaNO lowers NeurOLight's port-power error from 0.2018 to 0.0739 despite slightly higher cMAE, showing that global field accuracy alone is not sufficient for design-relevant readout fidelity. PaNO-R2 attains the best cMAE, propagation-profile error, output-profile error, and port-power error, reducing NeurOLight's port-power and output-profile errors by 72.7% and 72.5%.
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References · 10
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
- 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
- 2021Choose a Transformer: Fourier or Galerkincited by 333
- 2023GNOT: A General Neural Operator Transformer for Operator Learningcited by 321
- 2021Factorized Fourier Neural Operatorscited by 237
- 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
- 2022NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulationcited by 17
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