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.

arXiv commentAccepted for publication in Nano Letters

References in corpus (11)

• Dielectric Metasurfaces for Complete Control of Phase and Polarization with Subwavelength Spatial Resolution and High Transmission
• Outlook for inverse design in nanophotonics
• Training deep neural networks for the inverse design of nanophotonic structures
• Deep neural networks for the evaluation and design of photonic devices
• Deep learning in nano-photonics: inverse design and beyond
• A newcomer's guide to deep learning for inverse design in nano-photonics
• Data driven approaches in nanophotonics: A review of AI-enabled metadevices
• Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces
• Fixed-Attention Mechanism for Deep-Learning-Assisted Design of High-Degree-of-Freedom 3D Metamaterials
• A Self-Evolving Agentic Framework for Metasurface Inverse Design
• A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design