Exploiting geometric biases in inverse nano-optical problems using artificial neural networks.

Solving the inverse problem is a major challenge in contemporary nano-optics. However, frequently not just a possible solution needs to be found but rather the solution that accommodates constraints imposed by the problem at hand. To select the most plausible solution for a nano-optical inverse problem additional information can be used in general, but how to specifically formulate it frequently remains unclear. Here, while studying the reconstruction of the shape of an object using the electromagnetic field in its proximity, we show how to take advantage of artificial neural networks (ANNs) to produce solutions consistent with prior assumptions concerning the structures. By preparing suitable datasets where the specific shapes of possible scatterers are defined, the ANNs learn the underlying scatterer present in the datasets. This helps to find a plausible solution to the otherwise non-unique inverse problem. We show that topology optimization, in contrast, can fail to recover the scatterer geometry meaningfully but a hybrid approach that is based on both, ANNs and a topology optimization, eventually leads to the most promising performance. Our work has direct implications in fields such as optical metrology.

Inverse design of core-shell particles with discrete material classes using neural networks.

The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1-2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers.

Artificial neural networks used to retrieve effective properties of metamaterials.

We propose using deep neural networks for the fast retrieval of effective properties of metamaterials based on their angular-dependent reflection and transmission spectra from thin slabs. While we noticed that non-uniqueness is an issue for a successful application, we propose as a solution an automatic algorithm to subdivide the entire parameter space. Then, in each sub-space, the mapping between the optical response (complex reflection and transmission coefficients) and the corresponding material parameters (dielectric permittivity and permeability) is unique. We show that we can easily train one neural network per sub-space. For the final parameter retrieval, predictions from the different sub-networks are compared, and the one with the smallest error expresses the desired effective properties. Our approach allows a significant reduction in run-time, compared to more traditional least-squares fitting. Using deep neural networks to retrieve effective properties of metamaterials is a significant showcase for the application of AI technology to nanophotonic problems. Once trained, the nets can be applied to retrieve properties of a larger number of different metamaterials.

Hyperbolic surface waves on anisotropic materials without hyperbolic dispersion.

We theoretically analyze directional surface electromagnetic waves supported at an interface between an isotropic medium and anisotropic metal with effective uniaxial negative permittivity. We identify two types of surface wave solutions, resulting in unique hyperbolic dispersion in the wavevector space. Such anisotropic metal can be realized by alternating dielectric and metallic layers with deep subwavelength thicknesses or metallic nanowires in dielectric host. Such systems serve as a platform for many applications in nanophotonics.

Boosting Light Emission from Single Hydrogen Phthalocyanine Molecules by Charging.

Interest in electroluminescence of single molecules is stimulated by the prospect of possible applications in novel light emitting devices. Recent studies provide valuable insights into the mechanisms leading to single molecule electroluminescence. Concrete information on how to boost the intensity of the emitted light, however, is rare. By combining scanning tunnelling microscopy (STM) and quantum chemical calculations, we show that the light emission efficiencies of an individual hydrogen-phthalocyanine molecule can be increased by a factor of ≈19 upon charging. This boost in intensity can be explained by the development of a vertical dipole moment normal to the substrate facilitating out-coupling of the local excitation to the far field. As this effect is not related to the specific nature of hydrogen-phthalocyanine, it opens up a general way to increase light emission from molecular junctions.

Optical reconfiguration and polarization control in semi-continuous gold films close to the percolation threshold.

Controlling and confining light by exciting plasmons in resonant metallic nanostructures is an essential aspect of many new emerging optical technologies. Here we explore the possibility of controllably reconfiguring the intrinsic optical properties of semi-continuous gold films, by inducing permanent morphological changes with a femtosecond (fs)-pulsed laser above a critical power. Optical transmission spectroscopy measurements show a correlation between the spectra of the morphologically modified films and the wavelength, polarization, and the intensity of the laser used for alteration. In order to understand the modifications induced by the laser writing, we explore the near-field properties of these films with electron energy-loss spectroscopy (EELS). A comparison between our experimental data and full-wave simulations on the exact film morphologies hints toward a restructuring of the intrinsic plasmonic eigenmodes of the metallic film by photothermal effects. We explain these optical changes with a simple model and demonstrate experimentally that laser writing can be used to controllably modify the optical properties of these semi-continuous films. These metal films offer an easy-to-fabricate and scalable platform for technological applications such as molecular sensing and ultra-dense data storage.

Dark-field hyperlens: Super-resolution imaging of weakly scattering objects.

We propose a device for subwavelength optical imaging based on a metal-dielectric multilayer hyperlens designed in such a way that only large-wavevector (evanescent) waves are transmitted while all propagating (small-wavevector) waves from the object area are blocked by the hyper-lens. We numerically demonstrate that as the result of such filtering, the image plane only contains scattered light from subwavelength features of the objects and is completely free from background illumination. Similar in spirit to conventional dark-field microscopy, the proposed dark-field hyperlens is shown to enhance the subwavelength image contrast by more than two orders of magnitude. These findings are essential for optical imaging of weakly scattering subwavelength objects, such as real-time dynamic nanoscopy of label-free biological objects.