Inverse Design of Diffractive Metasurfaces Using Diffusion Models
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Metasurfaces are ultra-thin optical elements composed of engineered
sub-wavelength structures that enable precise control of light. Their inverse
design - determining a geometry that yields a desired optical response - is
challenging due to the complex, nonlinear relationship between structure and
optical properties. This often requires expert tuning, is prone to local
minima, and involves significant computational overhead. In this work, we
address these challenges by integrating the generative capabilities of
diffusion models into computational design workflows. Using an RCWA simulator,
we generate training data consisting of metasurface geometries and their
corresponding far-field scattering patterns. We then train a conditional
diffusion model to predict meta-atom geometry and height from a target spatial
power distribution at a specified wavelength, sampled from a continuous
supported band. Once trained, the model can generate metasurfaces with low
error, either directly using RCWA-guided posterior sampling or by serving as an
initializer for traditional optimization methods. We demonstrate our approach
on the design of a spatially uniform intensity splitter and a polarization beam
splitter, both produced with low error in under 30 minutes. To support further
research in data-driven metasurface design, we publicly release our code and
datasets.
