Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

100 Hertz frame-rate switching threedimensional orbital angular momentum multiplexing holography via cross convolution

The orbital angular momentum(OAM)of light has been implemented as an information carrier in OAM holography.Holographic information can be multiplexed in theoretical unbounded OAM channels,promoting the applications of optically addressable dynamic display and high-security optical encryption.However,the frame-rate of the dynamic extraction of the information reconstruction process in OAM holography is physically determined by the switching speed of the incident OAM states,which is currently below 30 Hz limited by refreshing rate of the phase-modulation spatial light modulator(SLM).Here,based on a cross convolution with the spatial frequency of the OAM-multiplexing hologram,the spatial frequencies of an elaborately-designed amplitude distribution,namely amplitude decoding key,has been adopted for the extraction of three-dimensional holographic information encoded in a specific OAM information channel.We experimentally demonstrated a dynamic extraction frame rate of 100 Hz from an OAM multiplexing hologram with 10 information channels indicated by individual OAM values from-50 to 50.The new concept of cross convolution theorem can even provide the potential of parallel reproduction and distribution of information encoded in many OAM channels at various positions which boosts the capacity of information processing far beyond the traditional decoding methods.Thus,our results provide a holographic paradigm for high-speed 3D information processing,paving an unprecedented way to achieve the high-capacity short-range optical communication system.

Fluorescence Nanoscopy in Neuroscience

Fluorescence nanoscopy provides imaging techniques that overcome the diffraction-limited resolution barrier in light microscopy,thereby opening up a new area of research in biomedical imaging in fields such as neuroscience.Here,we review the foremost fluorescence nanoscopy techniques,including descriptions of their applications in elucidating protein architectures and mobility,the real-time determination of synaptic parameters involved in neural processes,three-dimensional imaging,and the tracking of nanoscale neural activity.We conclude by discussing the prospects of fluorescence nanoscopy,with a particular focus on its deployment in combination with related techniques(e.g.,machine learning)in neuroscience.

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of freedom.However,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model.Here,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction losses.The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a classifier.The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space transmission.Moreover,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

Differential interference contrast phase edging net:an all-optical learning system for edge detection of phase objects

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.

Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

Optical machine learning has emerged as an important research area that,by leveraging the advantages inherent to optical signals,such as parallelism and high speed,paves the way for a future where optical hardware can process data at the speed of light.In this work,we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks.We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption.The decryptors,designed for operation in the near-infrared region,are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm achieving a neuron density of>500 million neurons per square centimetre.This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3,sensing4,medical diagnostics5 and computing6,7.

Three-dimensional direct laser writing of biomimetic neuron interfaces in the era of artificial intelligence:principles,materials,and applications

The creation of biomimetic neuron interfaces(BNIs)has become imperative for different research fields from neural science to artificial intelligence.BNIs are two-dimensional or three-dimensional(3D)artificial interfaces mimicking the geometrical and functional characteristics of biological neural networks to rebuild,understand,and improve neuronal functions.The study of BNI holds the key for curing neuron disorder diseases and creating innovative artificial neural networks(ANNs).To achieve these goals,3D direct laser writing(DLW)has proven to be a powerful method for BNI with complex geometries.However,the need for scaled-up,high speed fabrication of BNI demands the integration of DLW techniques with ANNs.ANNs,computing algorithms inspired by biological neurons,have shown their unprecedented ability to improve efficiency in data processing.The integration of ANNs and DLW techniques promises an innovative pathway for efficient fabrication of large-scale BNI and can also inspire the design and optimization of novel BNI for ANNs.This perspective reviews advances in DLW of BNI and discusses the role of ANNs in the design and fabrication of BNI.