The complex refractive index for low-loss materials is conventionally extracted by either approximate analytical formula or numerical iterative algorithm (such as Nelder-Mead and Newton-Raphson) based on the transmission-mode terahertz time domain spectroscopy (THz-TDS). A novel 4-layer neural network model is proposed to obtain optical parameters of low-loss materials with high accuracy in a wide range of parameters (frequency and thickness). Three materials (TPX, z-cut crystal quartz and 6H SiC) with different dispersions and thicknesses are used to validate the robustness of the general model. Without problems of proper initial values and non-convergence, the neural network method shows even smaller errors than the iterative algorithm. Once trained and tested, the proposed method owns both high accuracy and wide generality, which will find application in the multi-class object detection and high-precision characterization of THz materials.

Graphene metamaterials (MMs) have the potential to reconfigure and dynamically control terahertz (THz) waves. In this study, we conducted numerical investigations to explore the effects of externally applied magnetic fields up to 20 Tesla on the transmission properties of graphene patterned split ring resonator (GSRR) MMs in the THz region. We quantitatively compared the tunability of resonance amplitude and frequency in the co-polarized transmission component between the magnetic method and the traditional electrical approach. Our results demonstrate that magnetic tuning can effectively modulate the resonant properties of GSRR MMs. Furthermore, when combining electrical and magnetic tuning, we observed an enhancement in the polarization conversion ratio, as well as the achievement of a significant Faraday rotation angle of nearly 90 degrees in GSRR MMs. These findings indicate the potential of functional graphene-based THz devices, including switches, modulators, polarization converters, and sensors.

No abstract

No abstract