Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices

TL;DR

This paper introduces PIC-Flow, a physics-informed neural network that efficiently predicts electromagnetic fields in photonic devices, reducing reliance on computationally expensive simulations.

Contribution

The work presents a novel flow-based generative model incorporating physics constraints for accurate, fast electromagnetic field prediction in photonic device design.

Findings

PIC-Flow achieves accurate field predictions comparable to FDTD simulations.

The model generalizes to unseen device geometries like S-bends and tapers.

Physics-constrained training improves physical validity of predictions.

Abstract

Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple device geometries. We present PIC-Flow, a generative neural surrogate that predicts electromagnetic field distributions for photonic devices given their geometry and operating wavelength as an alternative to costly finite-difference time-domain (FDTD) simulations. Our approach combines three key ideas: (i) conditional flow matching as the generative framework, learning a velocity field that transports Gaussian noise to physically valid field solutions; (ii) a real-valued U-Net operating on split real and imaginary field channels; and (iii) physics-constrained training through a Helmholtz residual loss enforcing $\nabla^2 E_z + k_0^2 \varepsilon E_z = 0$. We introduce an interface-aware masking scheme for the Helmholtz residual that excludes dielectric boundary pixels where finite-difference stencil errors dominate, yielding a physically meaningful compliance metric. The data set consists of 22,500 ground-truth FDTD simulations split evenly between multimode interferometers, Y-branches, and directional couplers at $\lambda=1.55\,\mu$m in an 80/10/10 split between training, validation, and test sets. We evaluate ablations on the network against the held out test devices and also show that the model generalizes to held out device classes such as S-bends, tapers, and cascaded Y-branches. Rather than a drop-in replacement for FDTD, this work establishes a foundation that, with broader data coverage, more compute, and further training optimization, could scale toward broadband, device-agnostic field prediction with dramatically improved runtime for rapid design-space exploration of complex photonic devices and circuits.