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Spatial dispersion plays a critical role in nanophotonics when small plasmonic structures with feature sizes of few nanometers are handled. Such nonlocality is typically considered in a hydrodynamic framework and generally requires solving coupled partial differential equations, and therefore is involved. We develop a generalized local analogue model to reflect the nonlocal effects of plasmonic structures and avoid the complicated analysis within the multiple-fluid hydrodynamic framework, where more than one kind of charge carriers is considered. We show that spatial nonlocality can be represented by simply replacing the nonlocal surface region with an in-situ artificial local dispersive film. With such an elegant and simple-to-use alternative, the conventional analysis and simulations in the local regime acquire nonlocal capability, sufficient for a quantitative description of various plasmonic structures in nanoscale, rendering a much simpler process and great practical advantages in the numerical treatment.

In this article, we report a vector-network-analyzer-free and real-time LC wireless capacitance readout system based on perturbed nonlinear parity-time (PT) symmetry. The system is composed of two inductively coupled reader-sensor parallel RLC resonators with gain and loss, respectively. By searching for the real mode that requires the minimum saturation gain, the steady-state frequency evolution as a function of the sensor capacitance perturbation is analytically deduced. The proposed system can work in different modes by setting different perturbation points. In particular, at the exceptional point of PT symmetry, the system exhibits high sensitivity. Experimental demonstrations revealed the viability of the proposed readout mechanism by measuring the steady-state frequency of the reader resonator in response to the change of trimmer capacitor on the sensor side. Our findings could impact many emerging applications such as implantable medical device for health monitoring, parameter detection in harsh environment, sealed food packages, etc.

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With the flourishing development of nanophotonics, a Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding the swift particle. However, the radiation pattern can be sensitive to the geometry and material properties of PCs, such as periodicity, unit thickness, and dielectric fraction, making direct analysis and inverse design difficult. In this paper, we propose a systematic method to analyze and design PC-based transition radiation, which is assisted by deep learning neural networks. By matching boundary conditions at the interfaces, effective Cherenkov radiation of multilayered structures can be resolved analytically using the cascading scattering matrix method, despite the optical axes not being aligned with the swift electron trajectory. Once properly trained, forward deep learning neural networks can be utilized to predict the radiation pattern without further direct electromagnetic simulations. In addition, tandem neural networks have been proposed to inversely design the geometry and/or material properties for the desired effective Cherenkov radiation pattern. Our proposal demonstrates a promising strategy for dealing with layered-medium-based effective Cherenkov radiation detectors, and it can be extended to other emerging metamaterials, such as photonic time crystals.