Diffusion model-based inverse design of photonic crystals for customized refraction.

ABSTRACT: Photonic crystals (PhCs) have demonstrated great potential for use in integrated photonic systems. However, traditional design methods often struggle with low efficiency and limited flexibility. While deep learning approaches offer innovative solutions for the inverse design, existing generative models like generative adversarial network and variational autoencoders still face challenges such as training instability or excessive noise. Here, a novel generative design framework based on the diffusion model is presented to achieve the inverse design of high-precision and customized refraction structures. A comprehensive dataset consisting of operating frequency, refracted angles and corresponding structure patterns is constructed by calculating the equifrequency contours of various PhCs at a

SUBMITTER: Lin R 

PROVIDER: S-EPMC12717900 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature