[2604.01106] Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models

[2604.01106] Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2604.01106: Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models

Physics > Optics arXiv:2604.01106 (physics) [Submitted on 1 Apr 2026] Title:Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models Authors:Jonas Schaible, Asena Karolin Özdemir, Charlotte Debus, Sven Burger, Achim Streit, Christiane Becker, Klaus Jäger, Markus Götz View a PDF of the paper titled Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models, by Jonas Schaible and 7 other authors View PDF 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, includi...

Originally published on April 02, 2026. Curated by AI News.

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