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.

Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling power.Despite various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in multidimension.Here we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,simultaneously.This selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art results.The designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area reduction.This research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

High performance“non-local”generic face reconstruction model using the lightweight Speckle-Transformer(SpT)UNet

Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.

Giant saturation absorption of tungsten trioxide film prepared based on the seedless layer hydrothermal method

Nonlinear materials have gained wide interest as saturable absorbers and pulse compression for pulsed laser applications due to their unique optical properties.This work investigates the third-order nonlinear phenomenon of tungsten trioxide(WO_(3))thin films.The giant nonlinear absorption and nonlinear refractive index of WO_(3)thin films were characterized by Z-scan method at 800 nm.We experimentally observed the giant saturable absorption(SA)and nonlinear refractive index of WO_(3)thin films prepared by the seedless layer hydrothermal method,with SA coefficient being as high as-2.59×105cm·GW^(-1).The SA coefficient is at least one order of magnitude larger than those of the conventional semiconductors.The nonlinear refractive index n_(2)of WO_(3)film has been observed for the first time in recent studies and the corresponding coefficient can be up to 1.793 cm^(2)·GW^(-1).The large third-order nonlinear optical(NLO)response enables WO_(3)thin films to be promising candidates for optoelectronic and photonic applications in the near-infrared domain.

Laser scribed graphene for supercapacitors

Supercapacitors,with the merits of both capacitors for safe and fast charge and batteries for high energy storage have drawn tremendous attention.Recently,laser scribed graphene has been increasingly studied for supercapacitor applications due to its unique properties,such as flexible fabrication,large surface area and high electrical conductivity.With the laser direct writing process,graphene can be directly fabricated and patterned as the supercapacitor electrodes.In this review,facile laser direct writing methods for graphene were firstly summarized.Various precursors,mainly graphene oxide and polyimide were employed for laser scribed graphene and the modifications of graphene properties were also discussed.This laser scribed graphene was applied for electrochemical double-layer capacitors,pseudo-capacitors and hybrid supercapacitors.Diverse strategies including doping,composite materials and pattern design were utilized to enhance the electrochemical performances of supercapacitors.Featured supercapacitors with excellent flexible,ultrafinestructured and integrated functions were also reviewed.

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.

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.

Scalable and flexible porous hybrid film as a thermal insulating subambient radiative cooler for energy-saving buildings

Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon neutrality.Toward real-world applications,however,the parasitic heat input and heat leakage pose crucial challenges to commercial and residential buildings cooling.The integrating of radiative cooling and thermal insulation properties represents an attractive direction in renewable energy-efficient building envelope materials.Herein,we present a hierarchically porous hybrid film as a scalable and flexible thermal insulating subambient radiative cooler via a simple and inexpensive inverse high internal phase emulsion strategy.The as-prepared porous hybrid film exhibits an intrinsic combination of high solar reflectance(0.95),strong longwave infrared thermal emittance(0.97),and low thermal conductivity(31 mW/(m K)),yielding a subambient cooling temperature of~8.4℃ during the night and~6.5℃ during the hot midday with an average cooling power of~94 W/m^(2) under a solar intensity of~900 W/m^(2).Promisingly,combining the superhydrophobicity,durability,superelasticity,robust mechanical strength,and industrial applicability,the film is favorable for large-scale,sustainable and energy-saving applications in a wide variety of climates and complicated surfaces,enabling a substantial reduction of energy costs,greenhouse gas emission and associated ozone-depleting from traditional cooling systems.

Laser-scribed graphene for sensors:preparation,modification,applications,and future prospects

Sensors are widely used to acquire biological and environmental information for medical diagnosis,and health and environmental monitoring.Graphene is a promising new sensor material that has been widely used in sensor fabrication in recent years.Compared with many other existing graphene preparation methods,laser-scribed graphene(LSG)is simple,low-cost,environmentally friendly,and has good conductivity and high thermal stability,making it widely used in the sensor field.This paper summarizes existing LSG methods for sensor fabrication.Primary LSG preparation methods and their variants are introduced first,followed by a summary of LSG modification methods designed explicitly for sensor fabrication.Subsequently,the applications of LSG in stress,bio,gas,temperature,and humidity sensors are summarized with a particular focus on multifunctional integrated sensors.Finally,the current challenges and prospects of LSG-based sensors are discussed.

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.

Photonic matrix multiplication lights up photonicaccelerator and beyond

Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms.Photonic accelerators are designed to accelerate specific categories of computing in the optical domain,especially matrix multiplication,to address the growing demand for computing resources and capacity.Photonic matrix multiplication has much potential to expand the domain of telecommunication,and artificial intelligence benefiting from its superior performance.Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors.In this review,we first introduce the methods of photonic matrix multiplication,mainly including the plane light conversion method,Mach–Zehnder interferometer method and wavelength division multiplexing method.We also summarize the developmental milestones of photonic matrix multiplication and the related applications.Then,we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years.Finally,we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

High-dimensional orbital angular momentum multiplexing nonlinear holography

Nonlinear holography has been identified as a vital platform for optical multiplexing holography because of the appearance of new optical frequencies.However,due to nonlinear wave coupling in nonlinear optical processes,the nonlinear harmonic field is coupled with the input field,laying a fundamental barrier to independent control of the interacting fields for holography.We propose and experimentally demonstrate high-dimensional orbital angular momentum(OAM)multiplexing nonlinear holography to overcome this problem.By dividing the wavefront of the fundamental wave into different orthogonal OAM channels,multiple OAM and polarization-dependent holographic images in both the fundamental wave and second-harmonic wave have been reconstructed independently in the spatial frequency domain through a type-II second harmonic generation process.Moreover,this method can be easily extended to cascadedχ2 nonlinear optical processes for multiplexing in more wavelength channels,leading to potential applications in multicasting in optical communications,multiwavelength display,multidimensional optical storage,anticounterfeiting,and optical encryption.

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.

Three-Dimensional Direct Laser Writing of PEGda Hydrogel Microstructures with Low Threshold Power using a Green Laser Beam

Three-dimensional(3D)direct laser writing(DLW)based on two-photon polymerisation(TPP)is an advanced technology for fabricating precise 3D hydrogel micro-and nanostructures for applications in biomedical engineering.Particularly,the use of visible lasers for the 3D DLW of hydrogels is advantageous because it enables high fabrication resolution and promotes wound healing.Polyethylene glycol diacrylate(PEGda)has been widely used in TPP fabrication owing to its high biocompatibility.However,the high laser power required in the 3D DLW of PEGda microstructures using a visible laser in a high-water-content environment limits its applications to only those below the biological laser power safety level.In this study,a formula for a TPP hydrogel based on 2-hydroxy-2-methylpropiophenone(HMPP)and PEGda was developed for the fabrication of 3D DLW microstructures at a low threshold power(0.1 nJ per laser pulse at a writing speed of 10μm·s^(−1))in a high-water-content environment(up to 79%)using a green laser beam(535 nm).This formula enables the fabrication of microstructures with micrometre fabrication resolution and high mechanical strength(megapascal level)and is suitable for the fabrication of waterresponsive,shape-changing microstructures.These results will promote the utilisation of low-power 3D DLW for fabricating hydrogel microstructures using visible lasers in high-water-content environments.

Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition

A memristor is a promising candidate of new electronic synaptic devices for neuromorphic computing.However,conventional memristors often exhibit complex device structures,cumbersome manufacturing processes,and high energy consumption.Graphene-based materials show great potential as the building materials of memristors.With direct laser writing technology,this paper proposes a lateral memristor with reduced graphene oxide(rGO)and Pt as electrodes and graphene oxide(GO)as function material.This Pt/GO/rGO memristor with a facile lateral structure can be easily fabricated and demonstrates an ultra-low energy consumption of 200 nW.Typical synaptic behaviors are successfully emulated.Meanwhile,the Pt/GO/rGO memristor array is applied in the reservoir computing network,performing the digital recognition with a high accuracy of 95.74%.This work provides a simple and low-cost preparation method for the massive production of artificial synapses with low energy consumption,which will greatly facilitate the development of neural network computing hardware platforms.

Phyllotaxis bionics for vortex nanosieves

eLight,1(5),2021 https://doi.org/10.1186/s43593-021-00005-9 Simultaneous generation of multiple optical vortex(OV)lies at the heart of the application for orbital angular momentum(OAM)multiplexing both in classical and quantum domains.Previous structure with segmented or interleaved functional sub-areas in a single nano-device has been developed at the cost of its compactness and channel capacity.Back-to-nature design inspired by the spiral phyllotaxis pattern in this work offers a fresh way to fabricate a truly space-degenerated nanoscale multipleOVs-generator,namely vortex nanosieves.

Denoised Internal Models:A Brain-inspired Autoencoder Against Adversarial Attacks

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest ones.Inspired by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this challenge.Simulating the pipeline in the human brain for visual signal processing,DIM adopts a two-stage approach.In the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the thalamus.Inspired by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each category.We evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

AgInS_(2)/ZnS quantum dots for noninvasive cervical cancer screening with intracellular pH sensing using fluorescence lifetime imaging microscopy

Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various diseases.Here,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long fluorescence lifetimes of hundreds of nanoseconds and low toxicity.Fluorescence lifetime imaging microscopy(FLIM)combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell lines.The FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions,such as in lysosomes or cytoplasm.This method can also distinguish cancer cells from normal cells,and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100±7 ns.Most importantly,the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS QDs.The lifetime difference value between the normal and cervical cancer(CC)groups is 115±9 ns,and the difference between the normal and the precancerous lesion group is 64±9 ns.For the first time,the noninvasive method has been used for cervical cancer screening,and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.