From the birth of plasmonics, the generation of hot carriers in nanostructured metals has been recognized as a fundamental challenge towards effectively harnessing light energy stored in sub-diffraction plasmon modes. However, the observation of hot-carrier transport at metal/dielectric Schottky junctions has reframed this challenge as a distinctive opportunity to facilitate precise control over photochemical and photophysical processes in a manner that is both spectrally selective and spatially precise. To further diversify the array of prospective applicationsin this research area, we showcase the generation of terahertz (THz) electromagnetic waves using the ultrafast formation and interfacial transport of plasmonic hot carriers in hybrid metal/dielectric nano-systems. The introduced hot-carrier-based coherent THz sources mitigate stringent materials requirements pertinent to state-of-the-art technologies for producing THz waves.
In this paper, we present an approach to improving the performance of surface relief type augmented reality (AR) glasses by introducing the gap plasmonic mode at the input coupling grating. Our results demonstrate that the gap plasmonic mode interacts with the incoming light from the light engine, leading to a significant increase in diffraction efficiency at the desired wavelength range. Moreover, the proposed structure exhibits a high diffraction efficiency at a wide angle of incidence, thanks to the magnetic mie resonance mode at the gap plasmonic mode. We used a DOE waveguide consisting of an incoupler and an outcoupler and compared the efficiency by measuring the eyebox efficiency at a certain distance from the outcoupler. The metal used for deposition was silver, and we observed a final efficiency increase of 3-4 times.
In this work, we report a novel method to mitigate the dependency of incident angle on diffraction efficiency by depositing a metal film on the grating structure. Moreover, the metallic structures strongly enhance the interaction between the incoming light and the nanostructures, leading to an improvement in diffraction efficiency. The results have shown the potential to overcome the bottleneck of augmented reality glasses, which require a large field of view with high efficiency.
Traditional processes for the design of metamaterial structures are often computational heavy, time-consuming, and occasionally does not lead to the desired optical response. Deep learning can quickly optimize structures through inverse design, and create new geometries for devices. This research uses a deep learning framework for the inverse design of an optimal plasmonic structure to maximize the second-order nonlinear response from a nonlinear metamaterial. The thinfilm nonlinear metamaterial employed is a nanolaminate, and the optimal plasmonic structure is fabricated to establish the validity of the deep learning algorithm.
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