By verifying the electromagnetic response characteristics of graphene in the low terahertz (THz) band, a terahertz metamaterial sensor is proposed. The unit cell of the metamaterial sensor is a split ring resonator nested square ring resonator. The split ring resonator with four gaps is made of lossy metal, and the square ring resonator is formed by graphene. This structure can produce two high-performance resonant valleys in the transmission spectrum of 0.1–1.9 THz. The quantum interference between metal–graphene hybrid units also produces a reverse electromagnetically induced transparency (EIT)-like resonant peak between the two resonant valleys. Compared with the bimetallic ring resonator having the same shape and size, the sensor can dynamically adjust the position of the lower frequency resonant valley, thus, realizing the active tuning of the bandwidth and amplitude of the EIT-like resonant peak. The results demonstrate that the proposed sensor has a better sensing performance and can improve the detection precision by tuning itself to avoid the interference of environmental factors and the properties of samples. Combined with the advantages of convenience, rapidity, and non-damage of terahertz spectrum detection, the sensor has a good application potential to improve the unlabeled trace matter detection.

The simulation design of terahertz metamaterial sensors with dynamically tunable parameters typically relies on manual parameter tuning for structural optimization. However, this method is often prone to subjective factors and suffer from issues such as frequent reconstruction of simulations and suboptimal optimization results. In this paper, we propose a circuit analog optimization method (CAOM), which constructs equivalent RLC parameters to achieve a highly fitted terahertz transmission spectrum frequency obtained from CST full-wave numerical simulation. To validate the effectiveness of the proposed model, we use a typical periodic structure unit, a double-nested split ring resonator (DSRR) terahertz metamaterial sensor, as the simulation object. DSRR is made of graphene, as a result, the opening size and Fermi level of two resonant rings are dynamically tunable. The results of the validation demonstrate that the adjustments of the sensor parameters can be effectively mapped by the changes of the equivalent RLC parameters. And the proposed equivalent circuit model has parameter substitutability in the simulation modeling of split ring resonator type sensors. The proposed equivalent circuit model exhibits parameter substitution in the simulation modeling of open resonant ring-type sensors. To achieve optimal sensing performance for the electromagnetically induced transparency (EIT)-like resonant peak (with a resonant frequency of f2) of the sensor under constrained conditions, we introduce the genetic algorithm (GA) into the equivalent circuit model to enable fast optimization of the opening sizes of the inner and outer resonant rings, as well as the Fermi level of the sensor. Moreover, the accuracy of the optimization results is verified by CST simulations. Finally, the optimization results show that the optimal FOM of the EIT-like resonant peak within the given parameter range is 0.712, which is greater than that of any randomly combined parameters. This numerical result demonstrates the effectiveness of the proposed CAOM.