Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models

TL;DR

This paper introduces OptoLlama, a diffusion model that effectively infers multilayer optical film structures from spectral data, outperforming previous methods in accuracy and physical plausibility.

Contribution

The paper presents a novel masked diffusion language model for inverse optical design, demonstrating significant improvements over existing data-driven approaches.

Findings

Reduces spectral error by 2.9-fold compared to nearest-neighbor baseline.

Reduces spectral error by 3.45-fold compared to OptoGPT.

Recovers physically meaningful multilayer motifs, including distributed Bragg reflectors.

Abstract

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.