Abstract
In this article, we investigate the absorption characteristics of a graphene-embedded FP cavity in a terahertz spectral window. The optical attributes were determined by a 4 × 4 transfer matrix procedure. The findings demonstrate that perfect absorption is completely reliant on the structural characteristics of the FP cavity throughout a broad range of terahertz frequencies. From the obtained dataset, numerical formulae are generated for structural parameters (N FD , N BD ) using a linear regression machine learning algorithm to achieve higher than 90% absorption. The artificial neural network trained using our dataset provided a coefficient of determination (R2)=1, opening up new pathways to design perfect terahertz absorbers. Furthermore, we explored the influence of magnetic biasing on absorption traits, and our findings show that fine absorption improvement is conceivable. The formulated numerical relations have greater importance in the design of perfect terahertz absorbers.
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