Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models

• URL: http://arxiv.org/abs/2504.17077v1
• Date: Wed, 23 Apr 2025 19:54:33 GMT
• Title: Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models
• Authors: Dongjin Seo, Soobin Um, Sangbin Lee, Jong Chul Ye, Haejun Chung,
• Abstract summary: We present AdjointDiffusion, a physics-guided framework that integrates adjoint gradient sensitivity into the sampling process of diffusion models.<n>Our method consistently outperforms state-of-the-art nonlinear gradient approaches in both efficiency and manufacturability.
• Score: 43.51581973358462
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (10^5 to 10^6). 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 additional post-processing. We demonstrate our method on two canonical photonic design problems--a bent waveguide and a CMOS image sensor color router--and show that our method consistently outperforms state-of-the-art nonlinear optimizers (such as MMA and SLSQP) in both efficiency and manufacturability, while using orders of magnitude fewer simulations (approximately 2 x 10^2) than pure deep learning approaches (approximately 10^5 to 10^6). By eliminating complex binarization schedules and minimizing simulation overhead, AdjointDiffusion offers a streamlined, simulation-efficient, and fabrication-aware pipeline for next-generation photonic device design. Our open-source implementation is available at https://github.com/dongjin-seo2020/AdjointDiffusion.

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