Reviews of Algorithm-Driven Terahertz Metamaterials: From Intelligent Design to Multidisciplinary Applications

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Abstract
Terahertz metamaterials, composed of subwavelength artificial structures, exhibit strongly nonlinear and tightly coupled electromagnetic responses governed by geometry, material properties, and resonance modes. Driven by growing demands in high-sensitivity sensing, terahertz communication, and functional imaging, the design space of these devices has rapidly expanded, rendering conventional empirical and parameter-sweeping approaches inefficient for global optimization. Algorithm-driven strategies, particularly those leveraging machine learning and deep learning, have emerged as powerful surrogates for electromagnetic simulation, enabling automated multi-objective optimization and rapid inverse design. Beyond predictive capabilities, these algorithms uncover latent physical mechanisms and support adaptive, closed-loop experimental implementations. Within a unified framework, this review systematically categorizes and summarizes advances in algorithm-driven design of terahertz metamaterials, including traditional optimization methods, machine learning, deep learning, and reinforcement learning, highlighting their roles in multi-layer structural design, multimodal coupling control, and dynamic multi-target detection. Finally, we conducted a detailed analysis of current challenges, including data quality, model generalization, and physical interpretability, and clearly identified future research directions. These directions are expected to lead to practical applications in high-sensitivity chemical and biological detection, terahertz wireless communication, and wave-based functional imaging.

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