Machine learning techniques using artificial neural networks (ANN) have proven to be extremely ef-fective in designing nanophotonic systems. This presentation focuses on two applications where ANNs are utilized for designing nanophotonic scatterers.
In the first scenario, ANNs act as surrogate solvers for Maxwell's equations, allowing the design of scatterers tailored to specific fabrication technologies like laser nanoprinting. Designing low-index material scatterers is complex, so solving the inverse problem multiple times from different starting points is crucial. A Fourier neural operator ANN serves as a surrogate Maxwell solver, simplifying this process.
The second scenario integrates ANNs into a holistic metasurface design framework. Individual meta-atoms are efficiently described by their scattering responses, typically expressed as polarizability or T-matrix that provide metasurfaces with functionality on demand. Then, suitably trained ANNs are used to identify feasible physical objects that offer the desired T-matrices.
We discuss our contributions to describe the optical response of photonic materials made from periodically arranged scatterers. These scatterers can be molecules or macroscopic objects. A unifying description is possible by representing the scatterers with a T-matrix. While considering the renormalization of the object’s T-matrix upon interaction with all scatterers in the lattice, any optical quantity of interest can be expressed on numerical grounds. We also derive analytical expressions for many of those quantities while considering scatterers up to octupolar order at normal and oblique incidence for subwavelength and diffracting metasurfaces. Exemplarily, design challenges using these methods are presented.
In this work, while exploiting the latest multiple scattering software that can handle up to a million of particles, we explore the possibility to observe Anderson localization of light in a disordered medium. The proposed method is an excellent tool to manipulate the multipolar response from the spheres such that regimes can be identified where light localization happens. Moreover, by calculating the mean free path in the cluster via simulation data, the localization wavelengths can be now effectively pinpointed. Both features could provide clear guidelines for future optical transmission experiments and for designs utilizing Anderson localization of light.
An efficient methodology for the modification of electrical resonators in order to be readily applicable at the
terahertz regime is developed in this paper. To this aim, the proposed miniaturization technique starts from the
conventional resonator which, without any change, exhibits the lowest possible electrical resonance for minimum
dimensions. Subsequently, a set of interdigital capacitors is embedded in the original structure to increase capaci-
tance, while their impact on the main resonance is investigated through computational simulations. Furthermore,
to augment the inductance of the initial resonator, and, hence reduce the resonance frequency, the concept of
spiral inductor elements is introduced. Again, results for the featured configuration with the additional elements
are numerically obtained and all effects due to their presence are carefully examined. Finally, the new alterations
are combined together and their in
influence on the resonance position and quality is thoroughly studied.
The present work investigates the propagation properties of the surface plasmon polariton wave supported on
graphene surface over an anisotropic substrate at far-infrared frequencies. Initially, the surface wave’s propagation
on isotropic media substrate is studied and verified with the theoretical estimation, including the noteworthy
epsilon-near-zero case. Moreover, after utilizing theoretical substrate media and examining anisotropy relative
to the normal to graphene’s surface, direction, the anisotropy is enforced to the tangential direction revealing
the significant influence of the substrate on the surface wave that is propagating on graphene. Additionally,
the more realistic implementation with graphene’s substrate consisting of metamaterial resonators is thoroughly
investigated. Numerical results are extracted through a reliable finite-difference time-domain (FDTD) algorithm,
focalising, mainly, on the wavelength of graphene’s surface wave.
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