Photonic funnels, conical waveguides with hyperbolic metamaterial (HMM) cores, efficiently focus mid-IR light to spatial areas much smaller than the free space wavelength. These devices were originally conceived as having a perfect electric conductor (PEC) coating to confine light as it propagates to their subwavelength tip, however, recent numerical analysis demonstrates that funnels without a conductive cladding exhibit peak intensities over 1,000 times greater than their clad counterparts while maintaining a confinement scale determined by their tip radius. The funnel's conical surface provides an oblique interface between the highly anisotropic HMM and an isotropic medium. This oblique interface enables anomalous reflections which reshape and redirect the incident beam towards the funnel tip. In this work we analyze how the gold cladding suppresses the field enhancement and demonstrate the importance of the anomalous reflection to the funnel's performance.
We aim to address one of the fundamental limitations of machine learning (ML): its reliance on extensive training datasets by incorporating physics-based intuition and Maxwell-equation-based constraints into ML process. We show that physics-guided networks require significantly smaller datasets, enable learning outside the original training data, and provide improved prediction accuracy and physics consistency. The proposed approaches are illustrated on examples of photonic composites, from photonic crystals to hyperbolic metamaterials.
We present an analytical formalism to predict the minimal thickness of a film, with an intrinsic absorption resonance, required to perfectly absorb all incident light. We show that on resonance, perfect absorption can be achieved at thicknesses well below one-thousandth of a free-space wavelength. The developed analytical formalism is validated numerically using rigorous coupled wave analysis and finite element techniques, and experimentally using thin-film superlattices of tin-doped indium oxide nanocrystals with collective plasmon resonances mimicking the absorption resonances considered in our analytical model. We further consider perfectly absorbing structures consisting of thin, non-resonant, but high loss, films, and show that perfect absorption can often only be achieved at film thicknesses well below what can be fabricated experimentally. We overcome this limitation by introducing the concept of thin-film dilution, and show, analytically, numerically, and experimentally, that these diluted films can accurately mimic the theoretical optical properties of nanometer, or even sub-atomic, thickness films. This work provides a path towards the rational design of ultra-thin absorbers for bolometric or non-linear optical applications.
Photonic funnels, conical waveguiding structures with hyperbolic metamaterial cores, have been proposed – and recently demonstrated at mid-infrared frequencies – as optical links between macro- and nano-scales. All recent realizations of the funnels utilize highly conductive claddings to prevent the leakage of light out of the core. Here we demonstrate that clad-less funnels can significantly outperform their PEC-clad counterparts due to excitation of novel surface modes. We also analyze funnel-light interaction in the time domain and demonstrate temporal separation between diffraction-limited and nano-confined signals. Perspectives of temporal shaping of the nano-confined radiation are also discussed.
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