Propagation of light near the band edge in one-dimensional multilayers

Optical systems offer rich modulation in light propagation, but sufficient quantitative descriptions lack when highly complex structures are considered since practical structures contain defects or imperfections. Here, we utilize a method combining a data-fitting method and a time-resolved system to describe light propagation near the band edges in onedimensional structures. Calculations after optimization of the method show little deviation to the measurements.

Full description of dipole orientation in organic light-emitting diodes

Considerable progress has been made in organic light-emitting diodes(OLEDs)to achieve high external quantum efficiency,among which dipole orientation has a remarkable effect.In most cases,the radiation of the dipoles in OLEDs is theoretically predicted with only one orientation parameter to match with corresponding experiments.Here,we develop a new theory with three orientation parameters to fully describe the relationship between dipole orientation and power density.Furthermore,we design an optimal test structure for measuring all three orientation parameters.All three orientation parameters could be retrieved from non-polarized spectra.Our theory provides a universal plot of dipole orientations in OLEDs,paving the way for designing more complicated OLED devices.

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.

Thin-film neural networks for optical inverse problem

The thin-film optical inverse problem has attracted a great deal of attention in science and industry,and is widely applied to optical coatings.However,as the number of layers increases,the time it takes to extract the parameters of thin films drastically increases.Here,we introduce the idea of exploiting the structural similarity of all-optical neural networks and applied it to the optical inverse problem.We propose thin-film neural networks(TFNNs)to efficiently adjust all the parameters of multilayer thin films.To test the performance of TFNNs,we implemented a TFNN algorithm,and a reflectometer at normal incidence was built.Operating on multilayer thin films with 232 layers,it is shown that TFNNs can reduce the time consumed by parameter extraction,which barely increased with the number of layers compared with the conventional method.TFNNs were also used to design multilayer thin films to mimic the optical response of three types of cone cells in the human retina.The light passing through these multilayer thin films was then recorded as a colored photo.