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A theoretical study of the optical properties of metallic nano-strip antennas is presented. Such strips exhibit retardation-based resonances resulting from the constructive interference of counter propagating short-range surface plasmon-polaritons (SR-SPPs) that reflect from the antenna terminations. A Fabry-P erot model was formulated that successfully predicts both the peak position and spectral shape of their optical resonances. This model requires knowledge of the SR-SPP reflection amplitude and phase pickup upon reflection from the structure terminations. These quantities were first estimated using an intuitive Fresnel reflection model and then calculated exactly using full-field simulations based on the finite-difference frequency-domain (FDFD) method. With only three dimensionless scaling parameters, the Fabry-P erot model provides simple design rules for engineering resonant properties of such plasmonic resonator antennas.

We report on the ability of resonant plasmonic slits to efficiently concentrate electromagnetic energy into a nanoscale volume of absorbing material placed inside or directly behind the slit. This gives rise to extraordinary optical absorption characterized by an absorption enhancement factor that well exceeds the enhancements seen for extraordinary optical transmission through slits. A semianalytic Fabry-Perot model for the resonant absorption is developed and shown to quantitatively agree with full-field simulations. We show that absorption enhancements of nearly 1000% can be realized at 633 nm for slits in aluminum films filled with silicon. This effect can be utilized in a wide range of applications, including high-speed photodetectors, optical lithography and recording, and biosensors.

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Although surface polariton modes supported by finite-width interfaces can guide electromagnetic energy in three dimensions, we demonstrate for the first time to our knowledge that such modes can be modeled by the solutions of two-dimensional dielectric slab waveguides. An approximate model is derived by a ray-optics interpretation that is consistent with previous investigations of the Fresnel relations for surface polariton reflection. This model is compared with modal solutions for metal stripe waveguides obtained by full vectorial magnetic-field finite-difference methods. The field-symmetric modes of such waveguides are shown to be in agreement with the normalized dispersion relationship for analogous TE modes of dielectric slab waveguides. Lateral confinement is investigated by comparison of power-density profiles, and implications for the diffraction limit of guided polariton modes are discussed. © 2005 Optical Society of America OCIS codes: 130.2790, 160.3220, 160.3900, 240.5420, 240.6680, 240.6690. Surface plasmon polaritons and surface phonon polaritons have received much attention for their ability to guide electromagnetic energy. 1-3 Unlike dielectric waveguides, which confine volume electromagnetic waves to an optically dense core, these surface electromagnetic waves are localized at interfaces between dielectric materials and metals or ionic solids that support charge density oscillations. This surface localization has led researchers to explore the potential for transporting information via guided polariton modes with smaller spatial extents than can be achieved with diffraction-limited dielectric waveguides. 4The best-studied guided polariton modes involve surface plasmon polaritons supported by finite-width metal stripes.5 Although such modes have proved difficult to calculate, recent characterization by nearfield scanning optical microscopy has probed their localized light intensities.6 Based on published bound modal solutions, initial interpretation of these images led to conclusions that surface plasmon modes are inconsistent with dielectric waveguide theory. However, recent solutions for the experimentally relevant leaky modes may provide for an interpretation that is consistent with ray optics. 7 In this Letter we investigate the applicability of dielectric waveguide theory to surface polariton modes along finite-width interfaces. By combining previous research on the optics of surface polaritons 8 with a ray-optics model for guided waves, we demonstrate that an equivalent dielectric slab waveguide can be used to approximate the solutions of guided polariton modes.By definition, a guided mode is an eigenstate of the electromagnetic field that propagates in a specified direction (e.g., z) with a unique propagation constant (i.e., k z ϵ ␤ + i␣). For dielectric slab waveguides an exact analytical formulation for the modal solutions is possible. Nevertheless, to acquire a physical intuition for these waveguides, a ray-optics interpretation is often used. In this model the guided mode is define...

Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few minutes. Raw seismic data (terabytes) and required subsurface prediction (gigabytes) are enormous. This large-scale, spatially irregular time-series data poses seismic data ingestion (SDI) as an unconventional yet fundamental problem in DSPW. Current DL research is limited to small-scale simplified synthetic datasets as they treat seismic data like images and process them with convolution networks. Real seismic data, however, is at least 5D. Applying 5D convolutions to this scale is computationally prohibitive. Moreover, raw seismic data is highly unstructured and hence inherently non-image like. We propose a fundamental shift to move away from convolutions and introduce SESDI: Set Embedding based SDI approach. SESDI first breaks down the mammoth task of large-scale prediction into an efficient compact auxiliary task. SESDI gracefully incorporates irregularities in data with its novel model architecture. We believe SESDI is the first successful demonstration of end-to-end learning on real seismic data. SESDI achieves SSIM of over 0.8 on velocity inversion task on real proprietary data from the Gulf of Mexico and outperforms the state-of-the-art U-Net model on synthetic datasets.

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