High-Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning-Augmented Diffusion Model

Abstract

Terahertz (THz) metamaterials with high-figure-of-merit (high-FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low-asymmetry geometric structures, leaving high-asymmetry designs largely unexplored due to the inefficiency of trial-and-error-based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data-driven nature. Here, a novel prior knowledge-guided generative model augmented by a physics-constrained active learning mechanism to design high-asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high-FoM THz resonance and generates new high-asymmetry structures. To mitigate the limited number of classical structures, the generated high-asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high-asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning-based metamaterial design. The proposed scheme for generating high-asymmetry structures provides a new effective and efficient solution for high-FoM resonance, expanding applications in high-sensitivity THz metadevices.