Related papers: Machine Learning-Based Optimization of Chiral Phot…
Machine learning techniques can reveal hidden structure in large data amounts and can potentially extent or even replace analytical scientific methods. In nanophotonics, modes can increase the light yield from emitters located inside the…
Recently, Tang and Cohen (Science 332, 333, 2011) have demonstrated a scheme to enhance the chiral response of molecules, which relies on the use of circularly polarized light in a standing wave configuration. Here we show a new type of…
With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion,…
Efficient manipulation of the emission direction of a chiral nanoscale light source is significant for information transmission and on-chip information processing. Here, we propose a scheme to control the directionality of nanoscale chiral…
Optical approaches for wavefront shaping traditionally rely on phase modulation through holographic techniques. Shaping the phase determines a wave's diffraction and hence its intensity distribution in space. We instead show that shaping…
On-chip chiral quantum light-matter interfaces, which support directional interactions, provide a promising platform for efficient spin-photon coupling, non-reciprocal photonic elements, and quantum logic architectures. We present full-wave…
Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN…
The oblique helicoidal structure is formed in right-angle cholesterics under the applied electric field. The electric field changes the pitch and cone angle but preserves the single-harmonic modulation of the refractive index. As a result,…
Experimentally obtained X-ray diffraction (XRD) patterns can be difficult to solve, precluding the full characterization of materials, pharmaceuticals, and geological compounds. Herein, we propose a method based upon a multi-objective…
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the…
Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural network to handle all exposure problems. Moreover, the…
Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem. While significant progress has been made on inferring…
A computationally-fast inverse design method for nanophotonic structures is presented. The method is based on two complementary convex optimization problems which modify the dielectric structure and resonant field respectively. The design…
Chiral response is of tremendous importance to many fields, such as analytical chemistry, polarization manipulation and biological sensing. Here, a chiral metasurface based on rectangular holes is systematically investigated. The results…
Polarization plays crucial role in light-matter interactions; hence its overall manipulation is an essential key to unlock the versatility of light manufacturing, especially in femtosecond laser direct writing. Existing polarization-shaping…
Identifying the handedness of chiral molecules is of fundamental importance in chemistry, biology, pharmacy, and medicine. Nanophotonic structures allow us to control light at the nanoscale and offer powerful tools for chiral sensing,…
Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by…
Aberrations limit optical systems in many situations, for example when imaging in biological tissue. Machine learning offers novel ways to improve imaging under such conditions by learning inverse models of aberrations. Learning requires…
We design photonic crystal waveguides with efficient chiral light--matter interfaces that can be integrated with solid-state quantum emitters. By using glide-plane-symmetric waveguides, we show that chiral light-matter interaction can exist…
We show how both the ellipticity $\eta$ and degree of polarization $\textit{P}$ influences the extraordinary optical chirality properties of non-paraxial vortex beams. We find that, in stark contrast to paraxial optics and non-vortex modes,…