MetaGen: LLM-Driven Generative Framework for Intelligent Metasurface Element

Metasurfaces are a transformative class of artificial electromagnetic materials with significant potential in communication, sensing, and security. However, existing design methods require detailed physical properties as input and lack flexibility under complex constraints, limiting their applicability. In this paper, we propose MetaGen, a general, efficient, and user-friendly generation framework for intelligent metasurface elements. MetaGen employs a fine-tuned large language model to translate natural language instructions into formatted physical properties and integrates a diffusion-based model to generate metasurface elements. Furthermore, we develop a metasurface element dataset with granular frequency sampling and extended geometric parameters to enable MetaGen to learn the complex relationships between metasurface element geometries and electromagnetic responses. Experimental results demonstrate that MetaGen effectively satisfies complex constraints, achieving electromagnetic responses closely aligned with target specifications.