Addressing high-performance data sparsity in metasurface inverse design using multi-objective optimization and diffusion probabilistic models

Recent advancements in deep learning, particularly generative networks capable of producing high-freedom structures, have significantly enhanced the precise generation of meta-atoms. However, these methodologies typically rely on an abundance of high-performance data, which remains scarce in many practical design scenarios. To bridge this gap, our study introduces what we believe to be a novel approach that synergistically combines multi-objective optimization algorithms with an enhanced diffusion model featuring an attention mechanism, termed MetaDiffusion-Att. Using the complex design task of dual-polarized, wide-angle incidence, and broadband low-emissivity electromagnetic glass as an application example, we demonstrate the effectiveness of our method through qualitative and quantitative experiments. The introduced multi-objective optimization method significantly captures more high-performance samples while ensuring high degrees of freedom, compared to currently widely used generic dataset construction methods. The MetaDiffusion-Att model, improved by the introduced attention mechanism, significantly outperforms conventional WGAN-GP and conditional VAE methods in generation accuracy and quality under small datasets. Furthermore, the proposed method exhibits extrapolation capabilities, generating new structures with performance surpassing that of the dataset, further enriching the design space. This framework provides a promising solution for the inverse design of metasurfaces in challenging scenarios with sparse high-performance samples.