Towards a universal meta-optics solver via large language models

Abstract:Metasurface design increasingly requires fast models that can operate across structurally distinct device families, rather than retraining a separate surrogate for every geometry class. Conventional neural network surrogates often depend on fixed-dimensional descriptors, family-specific output formats, and repeated architecture tuning, which limits their scalability across heterogeneous meta-atoms. Here, we present a unified large language model (LLM) workflow for multi-family metasurface modeling and inverse-design. Geometries, design parameters, and optical response channels were converted into a shared instruction-following text format and used to fine-tune Gemma-2-9B across 8 metasurface families. Compared with single-family baselines, the joint model simultaneously predicted the optical responses of all metasurface families while reducing the MSE for each family by an average of 56.5%. The same representation was also used for inverse design. These results show that a shared sequence-based LLM interface can provide a practical route to cross-family metasurface design while reducing the need for task-specific surrogate architectures.

Optics (physics.optics); Machine Learning (cs.LG)

arXiv:2608.26417 [physics.optics]

(or arXiv:2608.26417v1 [physics.optics] for this version)

https://doi.org/10.48550/arXiv.2608.26417 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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