Unveiling photon-photon coupling induced transparency and absorption

Abstract

This study presents the theoretical foundations of analogous electromagnetically induced transparency and absorption, which we refer to as coupling induced transparency (CIT) and absorption (CIA), respectively, along with an exploration of the transition between these phenomena. We provide a concise phenomenological description with analytical expressions for transmission spectra and dispersion, elucidating how the interplay of coherent and dissipative interactions in a coupled system results in the emergence of level repulsion (LR) and attraction (LA), corresponding to CIT and CIA, respectively. This theory comprehensively captures both the phenomena while modelling the microstrip line loaded resonators and their couplings systematically. The model is validated through numerical simulations using a hybrid system comprising a split ring resonator (SRR) and an electric inductive-capacitive (ELC) resonator in planar geometry. We analyse two cases while keeping the ELC parameters constant, one involving a dynamic adjustment of the SRR size with a fixed split gap, and the other entailing a varying gap while maintaining a constant SRR size. Notably, in the first case, the dispersion profile of the transmission signal demonstrates LR, while the second case results in LA, effectively showcasing CIT and CIA, respectively. These simulated findings not only align with the theoretical model but also underscore the versatility of our approach. Subsequently, we extend our model to a more general case, demonstrating that a controlled transition from CIT to CIA is achievable by manipulating the dissipation rate of individual modes within the hybrid system, leading to either coherent or dissipative interactions between the modes. Our results provide a pathway for designing hybrid systems that can control the group velocity of light, offering potential applications in the fields of optical switching and quantum information technology. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.