Systematic Review of Artificial Intelligent-Driven Inverse Design for Terahertz Metamaterials

Terahertz (THz) metamaterials, with their exceptional ability to precisely manipulate the phase, amplitude, polarization and orbital angular momentum (OAM) of electromagnetic waves, have demonstrated significant application potential across a wide range of fields. However, traditional design methodologies often rely on extensive parameter sweeps, making it challenging to address the increasingly complex and diverse application requirements. Recently, the integration of artificial intelligence (AI) techniques, particularly deep learning and optimization algorithms, has introduced new approaches for the design of THz metamaterials. This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies, with a particular focus on the advancements in AI-driven inverse design of THz metamaterials. The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials, thereby accelerating the design process and reducing both computational resources and time. It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials. Finally, we outline future research directions and technological challenges, with the goal of providing valuable insights and guidance for ongoing and future investigations.