Search for: All records

While spin angular momentum is limited to ±ℏ, orbital angular momentum (OAM) is, in principle, unbounded, enabling tailored optical transition rules in quantum systems. However, the large optical size of vortex beams hinders their coupling to nanoscale platforms such as quantum emitters. To address this challenge, we experimentally demonstrate the subdiffraction focusing of an OAM-carrying beam using a hypergrating, a flat meta-structure based on a multilayered hyperbolic composite. We show that our structure generates and guides high-wave vector modes to a deeply subwavelength spot and experimentally demonstrate the focus of an OAM-carrying beam on a spot size of ∼λ/3. We also show how the proposed platform facilitates the formation of an optical skyrmion with spin textures as small asλ/250, opening new avenues for controlling light–matter interactions. 

We experimentally demonstrate primordial metamaterials - composite media supporting essentially nonlocal wave propagation, grown with molecular beam epitaxy. Our transmission measurements confirm the theoretically predicted spectral signature of coupling to nonlocal modes. 

Abstract Photonic funnels, microscale conical waveguides that have been recently realized in the mid-IR spectral range with the help of an all-semiconductor designer metal material platform, are promising devices for efficient coupling of light between the nanoscales and macroscales. Previous analyses of photonic funnels have focused on structures with highly conductive claddings. Here, we analyze the performance of funnels with and without cladding, as a function of material properties, operating wavelength, and geometry. We demonstrate that bare (cladding-free) funnels enable orders-of-magnitude higher enhancement of local intensity than their clad counterparts, with virtually no loss of confinement, and relate this phenomenon to anomalous reflection of light at the anisotropic material–air interface. Intensity enhancement of the order of 25, with confinement of light to wavelength/20 scale, is demonstrated. Efficient extraction of light from nanoscale areas is predicted. 

We develop a hierarchical approach to building a Physics-guided neural network (PGNN) for scalable solutions of Maxwell equations with high spatial resolution and illustrate the developed formalism on a metamaterial photonic funnel example. 

Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network outputs with known physics), along with the supervision contained in data. Existing work in PGNNs has demonstrated the efficacy of adding single PG loss functions in the neural network objectives, using constant trade-off parameters, to ensure better generalizability. However, in the presence of multiple PG functions with competing gradient directions, there is a need to adaptively tune the contribution of different PG loss functions during the course of training to arrive at generalizable solutions. We demonstrate the presence of competing PG losses in the generic neural network problem of solving for the lowest (or highest) eigenvector of a physics-based eigenvalue equation, which is commonly encountered in many scientific problems. We present a novel approach to handle competing PG losses and demonstrate its efficacy in learning generalizable solutions in two motivating applications of quantum mechanics and electromagnetic propagation. All the code and data used in this work are available at https://github.com/jayroxis/Cophy-PGNN. 

Far-field analysis of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new technique capable of fast and accurate characterization of two-dimensional structures with at least wavelength/25 theoretical resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180-nm-scale features with 845-nm laser light, reaching a resolution of wavelength/5. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new optical characterization tools with high spatial resolution, fast data acquisition and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscopy 

Abstract Efficient optical coupling between nano‐ and macroscale areas is strongly suppressed by the diffraction limit. This work presents a possible solution to this fundamental problem via the experimental fabrication, characterization, and comprehensive theoretical analysis of structures referred to as “photonic funnels.” The funnels represent a novel composite material platform that combines hyperbolic dielectric response with geometry‐assisted optical confinement. Experimentally, funneling of mid‐infrared light through openings with diameters as small as 1/25th of the free space wavelength (λ₀) is demonstrated. By analyzing the optical response of the funnels, as fabricated, both confinement of mid‐infrared radiation to the λ₀/25 areas and efficient outcoupling of light from deep subwavelength areas are confirmed.