Momentum-space imaging spectroscopy for the study of nanophotonic materials

The novel phenomena in nanophotonic materials, such as the angle-dependent reflection and negative refraction effect, are closely related to the photonic dispersions EepT. EepT describes the relation between energy E and momentum p of photonic eigenmodes, and essentially determines the optical properties of materials. As EepT is defined in momentum space(k-space), the experimental method to detect the energy distribution, that is the spectrum, in a momentum-resolved manner is highly required. In this review, the momentum-space imaging spectroscopy(MSIS) system is presented, which can directly study the spectral information in momentum space. Using the MSIS system, the photonic dispersion can be captured in one shot with high energy and momentum resolution. From the experimental momentumresolved spectrum data, other key features of photonic eigenmodes, such as quality factors and polarization states, can also be extracted through the post-processing algorithm based on the coupled mode theory. In addition, the interference configurations of the MSIS system enable the measurement of coherence properties and phase information of nanophotonic materials, which is important for the study of light-matter interaction and beam shaping with nanostructures. The MSIS system can give the comprehensive information of nanophotonic materials, and is greatly useful for the study of novel photonic phenomena and the development of nanophotonic technologies.

Photonic-dispersion neural networks for inverse scattering problems

Inferring the properties of a scattering objective by analyzing the optical far-field responses within the framework of inverse problems is of great practical significance.However,it still faces major challenges when the parameter range is growing and involves inevitable experimental noises.Here,we propose a solving strategy containing robust neuralnetworks-based algorithms and informative photonic dispersions to overcome such challenges for a sort of inverse scattering problem—reconstructing grating profiles.Using two typical neural networks,forward-mapping type and inverse-mapping type,we reconstruct grating profiles whose geometric features span hundreds of nanometers with nanometric sensitivity and several seconds of time consumption.A forward-mapping neural network with a parameters-to-point architecture especially stands out in generating analytical photonic dispersions accurately,featured by sharp Fano-shaped spectra.Meanwhile,to implement the strategy experimentally,a Fourier-optics-based angle-resolved imaging spectroscopy with an all-fixed light path is developed to measure the dispersions by a single shot,acquiring adequate information.Our forward-mapping algorithm can enable real-time comparisons between robust predictions and experimental data with actual noises,showing an excellent linear correlation(R2>0.982)with the measurements of atomic force microscopy.Our work provides a new strategy for reconstructing grating profiles in inverse scattering problems.