Design and Machine Learning Optimization of a Dynamically Tunable VO2-Integrated Broadband Metamaterial Absorber for THz

This paper introduces a vanadium dioxide-integrated broadband metamaterial absorber designed for the terahertz frequency range. The simulation results for the proposed structure demonstrate a wide 90% absorption bandwidth of 8.23 THz, corresponding to a fractional bandwidth of 89.5%. By leveraging the phase-transition properties of VO2, the absorber demonstrated dynamic adjustability by modulating the absorption from 3% to 98.74%. The absorption mechanism was analyzed through the impedance matching theory and electromagnetic field distributions, confirming the role of magnetic resonance and interference. Furthermore, machine learning algorithms, specifically Linear Regression, Support Vector Regression, and Random Forest (RF), were applied to accelerate the design process and optimize the structural parameters. Among these, the RF model demonstrated superior prediction accuracy. The machine learning-assisted optimization successfully extended the effective absorption bandwidth to 9 THz, representing an improvement by 9.4% compared to the traditional optimization methods. These results validate the efficacy of combining electromagnetic simulation with data-driven techniques for advanced metamaterial design.