سمینار عمومی هفتگی ۱۰ خرداد ۱۴۰۴

عنوان: Tunable Optical Properties and Spectral Statistics in Graphene-Based One-Dimensional Defective Photonic Crystals

​​​​​​​سخنران: دکتر  زیبا سالکی (Zheijian Sci-Tech University, China)

شنبه ۱۰ خرداد ۱۴۰۴، ساعت ۱۵، آمفی‌تئاتر دانشکده

چکیده:
​​​​​​​Photonic crystals (PCs) are periodic dielectric structures that manipulate light through photonic band gaps (PBGs). Introducing defect layers breaks periodicity and creates localized defect modes,  enabling enhanced control over light propagation. This study investigates one-dimensional (1D) PCs  incorporating graphene-based hyperbolic metamaterials and vanadium dioxide (VO₂) defect layers,  focusing on their tunable optical properties in the terahertz (THz) and near-infrared regimes. The  optical properties are analyzed using the transfer matrix method alongside effective medium theory to model the complex layered structures accurately. We demonstrate novel defect modes influenced by the orientation of the hyperbolic optical axis, enabling polarization-dependent and dynamically tunable transmission controlled via graphene’s chemical potential. Notably, we reveal a broadband THz polarizing beam splitter and a unique graphene-induced Brewster angle phenomenon. Beyond linear optics, strong nonlinear absorption in VO₂-based PCs yields tunable, near-perfect narrowband absorption suitable for infrared sensing and switching applications. A significant contribution is the application of random matrix theory (RMT) to characterize spectral statistics, revealing transitions between chaotic and regular regimes modulated by parameters such as incident angle and chemical potential. Extending beyond photonics, we combine RMT with machine learning to analyze complex systems in finance, illustrating a novel data-driven framework for predicting system behavior. This work provides a comprehensive framework for tunable photonic devices and highlights the interdisciplinary potential of statistical and machine learning methods in complex system analysis.