Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models
This addresses the challenge of designing optical stacks from spectra, offering a novel method for photonics researchers, though it is incremental as it builds on existing diffusion and sequence modeling approaches.
The paper tackled the inverse design of optical multilayer thin films by introducing OptoLlama, a masked diffusion model that reduces mean absolute spectral error by 2.9-fold compared to a baseline and 3.45-fold compared to the state-of-the-art method on a test set of 3,000 targets.
Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the large design space and non-unique solutions. We introduce \texttt{OptoLlama}, a masked diffusion language model for inverse thin-film design from optical spectra. Representing multilayer stacks as sequences of material-thickness tokens, \texttt{OptoLlama} conditions generation on reflectance, absorptance, and transmittance spectra and learns a probabilistic mapping from optical response to structure. Evaluated on a representative test set of 3,000 targets, \texttt{OptoLlama} reduces the mean absolute spectral error by 2.9-fold relative to a nearest-neighbor template baseline and by 3.45-fold relative to the state-of-the-art data-driven baseline, called \texttt{OptoGPT}. Case studies on designed and expert-defined targets show that the model reproduces characteristic spectral features and recovers physically meaningful stack motifs, including distributed Bragg reflectors. These results establish diffusion-based sequence modeling as a powerful framework for inverse photonic design.