Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model

TL;DR

This paper introduces a novel AI-based inverse design method using diffusion models that directly maps optical properties to subwavelength photonic structures, eliminating the need for iterative simulations and accelerating device development.

Contribution

The work presents a diffusion model approach for direct inverse design of photonic structures from optical properties, bypassing traditional simulation-based optimization.

Findings

High-fidelity structure generation guided by optical properties

Elimination of iterative forward simulations in design process

Accelerated photonic device research and development

Abstract

Subwavelength photonic structures and metamaterials provide revolutionary approaches for controlling light. The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices. However, most of the existing inverse design methods cannot realize direct mapping from optical properties to photonic structures but instead rely on forward simulation methods to perform iterative optimization. In this work, we exploit the powerful generative abilities of artificial intelligence (AI) and propose a practical inverse design method based on latent diffusion models. Our method maps directly the optical properties to structures without the requirement of forward simulation and iterative optimization. Here, the given optical properties can work as "prompts" and guide the constructed model to correctly "draw" the required photonic structures. Experiments show that our direct mapping-based inverse design method can generate subwavelength photonic structures at high fidelity while following the given optical properties. This may change the method used for optical design and greatly accelerate the research on new photonic devices.