  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.
