Interpretable geometry sensitivity for inverse design of integrated photonics

As an increasingly powerful technique in integrated photonics, inverse design uses optimization algorithms to automatically create compact, high-performance photonic structures, often yielding non-intuitive layouts far more compact than conventional designs J. S. Jensen and O. Sigmund, Laser Photonics Rev. 5, 308 (2011) and S. Molesky et al., Nat. Photonics 12, 659 (2018). While adjoint-based inverse design is a prominent optimization method, the resulting free-form layouts are difficult to interpret or diagnose under fabrication variability C. M. Lalau-Keraly et al., Opt. Express 21, 21693 (2013); L. Su et al., arXiv:1910.04829 [physics.app-ph (2019); and A. Y. Piggott et al., Nat. Photonics 9, 374 (2015)]. We present an experimentally validated interpretability workflow that produces pixel-level sensitivity maps directly on the binary mask of an inverse-designed device. Using wavelength-division demultiplexers at 1310/1550 nm as examples, we train a lightweight convolutional surrogate to regress figures of merit and apply integrated gradients to attribute predicted transmission to individual pixels. We demonstrate that high-attribution hotspots correspond to physically meaningful substructures, such as splitter hubs and high-curvature edges. Experimental results show that controlled perturbations in these high-sensitivity regions result in up to an ∼11× higher excess insertion loss compared to perturbations in non-sensitive regions, consistent with full-wave simulations. This approach adds a practical explainability layer to existing pipelines, offering a clear pathway toward foundry-compatible design-rule checking, targeted quality control, localized metrology, and fabrication-aware constraint allocation without modifying the underlying electromagnetic solver.