Physics-Guided and Fabrication-Aware Inverse Design of Photonic Devices Using Diffusion Models

Abstract

Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional inverse-design approaches-whether driven by human intuition, global optimization, or adjoint-based gradient methods-often involve intricate binarization and filtering steps, while recent deep-learning strategies demand prohibitively large numbers of simulations (105-106). To overcome these limitations, we present AdjointDiffusion, a physics-guided framework that integrates adjoint sensitivity gradients into the sampling process of diffusion models. AdjointDiffusion begins by training a diffusion network on a synthetic, fabrication-aware dataset of binary masks. During inference, we compute the adjoint gradient of a candidate structure and inject this physics-based guidance at each denoising step, steering the generative process toward high-Figure of Merit (FoM) solutions without requiring meticulous binarization or filtering. We show that our method achieves approximately 15% higher FoM at equal simulation cost compared to state-of-the-art nonlinear optimizers (e.g., Method of Moving Asymptotes (MMA), Sequential Least-Squares Quadratic Programming (SLSQP)), or requires about 3x fewer simulations to reach the same FoM, all while ensuring fabrication-aware manufacturability. Compared to pure deep-learning approaches, our method requires similar to 103x fewer simulations. By eliminating complex binarization schedules and minimizing simulation overhead, AdjointDiffusion offers a simulation-efficient and fabrication-aware inverse-design algorithm with the nonconvex optimization capabilities of deep learning. Our open-source implementation is available at https://github.com/dongjin-seo2020/AdjointDiffusion.