Artificial neural networks enabled by nanophotonics

Femtosecond laser direct nanolithography of perovskite hydration for temporally programmable holograms

Modern nanofabrication technologies have propelled significant advancement of high-resolution and optically thin holograms. However, it remains a long-standing challenge to tune the complex hologram patterns at the nanoscale for temporal light field control. Here, we report femtosecond laser direct lithography of perovskites with nanoscale feature size and pixel-level temporal dynamics control for temporally programmable holograms. Specifically, under tightly focused laser irradiation, the organic molecules of layered perovskites (PEA)2PbI4 can be exfoliated with nanometric thickness precision and subwavelength lateral size. This creates inorganic lead halide capping nanostructures that retard perovskite hydration, enabling tunable hydration time constant. Leveraging advanced inverse design methods, temporal holograms in which multiple independent images are multiplexed with low cross talk are demonstrated. Furthermore, cascaded holograms are constructed to form temporally holographic neural networks with programmable optical inference functionality. Our work opens up new opportunities for tunable photonic devices with broad impacts on holography display and storage, high-dimensional optical encryption and artificial intelligence.

A Zero-Voltage-Writing Artificial Nervous System Based on Biosensor Integrated on Ferroelectric Tunnel Junction

The artificial nervous system proves the great potential for the emulation of complex neural signal transduction. However, a more bionic system design for bio-signal transduction still lags behind that of physical signals, and relies on additional external sources. Here, this work presents a zero-voltage-writing artificial nervous system (ZANS) that integrates a bio-source-sensing device (BSSD) for ion-based sensing and power generation with a hafnium-zirconium oxide-ferroelectric tunnel junction (HZO-FTJ) for the continuously adjustable resistance state. The BSSD can use ion bio-source as both perception and energy source, and then output voltage signals varied with the change of ion concentrations to the HZO-FTJ, which completes the zero-voltage-writing neuromorphic bio-signal modulation. In view of in/ex vivo biocompatibility, this work shows the precise muscle control of a rabbit leg by integrating the ZANS with a flexible nerve stimulation electrode. The independence on external source enhances the application potential of ZANS in robotics and prosthetics.

Fully forward mode training for optical neural networks

Optical computing promises to improve the speed and energy efficiency of machine learning applications1-6. However, current approaches to efficiently train these models are limited by in silico emulation on digital computers. Here we develop a method called fully forward mode (FFM) learning, which implements the compute-intensive training process on the physical system. The majority of the machine learning operations are thus efficiently conducted in parallel on site, alleviating numerical modelling constraints. In free-space and integrated photonics, we experimentally demonstrate optical systems with state-of-the-art performances for a given network size. FFM learning shows training the deepest optical neural networks with millions of parameters achieves accuracy equivalent to the ideal model. It supports all-optical focusing through scattering media with a resolution of the diffraction limit; it can also image in parallel the objects hidden outside the direct line of sight at over a kilohertz frame rate and can conduct all-optical processing with light intensity as weak as subphoton per pixel (5.40 × 1018- operations-per-second-per-watt energy efficiency) at room temperature. Furthermore, we prove that FFM learning can automatically search non-Hermitian exceptional points without an analytical model. FFM learning not only facilitates orders-of-magnitude-faster learning processes, but can also advance applied and theoretical fields such as deep neural networks, ultrasensitive perception and topological photonics.

Influence of discontinuities on photonic waveguides

Fabrication-induced imperfections in photonic wire waveguides, such as roughness, stitching errors, and discontinuities, degrade their performance and thereby lower the yield of large-scale systems. This degradation is primarily due to the high insertion losses induced by imperfections, which scale nonlinearly with the index contrast in wire waveguides. Here we investigate the influence of discontinuities in photonic waveguides and later show a platform that is robust to fabrication imperfections. Our platform is based on an array of silicon nano-pillars, arranged to form a sub-wavelength (SW) grating waveguide. We focus on investigating the robustness by considering an abrupt break in the waveguide, as an extreme case of discontinuity. We show that sub-wavelength silicon waveguides are robust against unwanted large discontinuities relative to the operating wavelength. We measure a transmission loss of <2.2 dB at 1550 n m, for a discontinuity of length 2.1 μ m, when compared to more than 7 d B of loss in conventional silicon wire waveguides for the same discontinuity. Our results show that this mode of protection is broadband, covering the entire telecommunication band (λ =1500-1600 nm). We believe that this investigation of the influence of discontinuities in photonic waveguides could be a step toward the realization of low-loss optical waveguides.

Multiplexable all-optical nonlinear activator for optical computing

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all-optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth (∼11.24 GHz), low nonlinear threshold (∼2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all-optical neural networks.

Nanotechnological advances in cancer: therapy a comprehensive review of carbon nanotube applications

Nanotechnology is revolutionising different areas from manufacturing to therapeutics in the health field. Carbon nanotubes (CNTs), a promising drug candidate in nanomedicine, have attracted attention due to their excellent and unique mechanical, electronic, and physicochemical properties. This emerging nanomaterial has attracted a wide range of scientific interest in the last decade. Carbon nanotubes have many potential applications in cancer therapy, such as imaging, drug delivery, and combination therapy. Carbon nanotubes can be used as carriers for drug delivery systems by carrying anticancer drugs and enabling targeted release to improve therapeutic efficacy and reduce adverse effects on healthy tissues. In addition, carbon nanotubes can be combined with other therapeutic approaches, such as photothermal and photodynamic therapies, to work synergistically to destroy cancer cells. Carbon nanotubes have great potential as promising nanomaterials in the field of nanomedicine, offering new opportunities and properties for future cancer treatments. In this paper, the main focus is on the application of carbon nanotubes in cancer diagnostics, targeted therapies, and toxicity evaluation of carbon nanotubes at the biological level to ensure the safety and real-life and clinical applications of carbon nanotubes.

PMONN: an optical neural network for photonic integrated circuits based on micro-resonator

We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.

Photonic neuromorphic architecture for tens-of-task lifelong learning

Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

Vectorial adaptive optics: expanding the frontiers of optical correction

Researchers at the University of Oxford have introduced a groundbreaking technique called vectorial adaptive optics (V-AO), which extends the capabilities of traditional adaptive optics to correct for both polarization and phase aberrations. This novel approach opens new possibilities for manipulating the complex vectorial field in optical systems, enabling higher-dimensional feedback correction.

Deep neural network-based phase calibration in integrated optical phased arrays

Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams.

Programmable optical switching integrated chip for 4-bit binary true/inverse/complement code conversions based on fluorinated photopolymers

In this work, programmable optical switching integrated chips for 4-bit binary true/inverse/complement optical code conversions (OCCs) are proposed based on fluorinated photopolymers. Fluorinated bis-phenol-A novolac resin (FAR) with low absorption loss and fluorinated polyacrylate (FPA) with high thermal stability are self-synthesized as core and cladding layer, respectively. The basic architecture of operating unit for the photonic chip designed is composed of directional coupler Mach-Zehnder interferometer (DC-MZI) thermo-optic (TO) switching, X-junction, and Y-bunching waveguide structures. The waveguide module by cascading 16 operating units could realize OCCs function through optical transmission matrix. The response time of the 4-bit binary OCCs is measured as about 300 µs. The insertion loss and extinction ratio of the actual chip are obtained as about 10.5 dB and 15.2 dB, respectively. The electric driving power consumption for OCCs is less than 6 mW. The true/inverse/complement OCCs are achieved by the programmable modulation circuit. The proposed technique is suitable for achieving optical digital computing system with high-speed signal processing and low power consumption.

Photothermally Activated Artificial Neuromorphic Synapses

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. To incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to "activate" these neurons and store "memory" by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

Artificial-Neural-Network-Driven Innovations in Time-Varying Process Diagnosis of Low-K Oxide Deposition

To address the challenges in real-time process diagnosis within the semiconductor manufacturing industry, this paper presents a novel machine learning approach for analyzing the time-varying 10th harmonics during the deposition of low-k oxide (SiOF) on a 600 Å undoped silicate glass thin liner using a high-density plasma chemical vapor deposition system. The 10th harmonics, which are high-frequency components 10 times the fundamental frequency, are generated in the plasma sheath because of their nonlinear nature. An artificial neural network with a three-hidden-layer architecture was applied and optimized using k-fold cross-validation to analyze the harmonics generated in the plasma sheath during the deposition process. The model exhibited a binary cross-entropy loss of 0.1277 and achieved an accuracy of 0.9461. This approach enables the accurate prediction of process performance, resulting in significant cost reduction and enhancement of semiconductor manufacturing processes. This model has the potential to improve defect control and yield, thereby benefiting the semiconductor industry. Despite the limitations imposed by the limited dataset, the model demonstrated promising results, and further performance improvements are anticipated with the inclusion of additional data in future studies.

Proteinoid Microspheres as Protoneural Networks

Proteinoids, also known as thermal proteins, possess a fascinating ability to generate microspheres that exhibit electrical spikes resembling the action potentials of neurons. These spiking microspheres, referred to as protoneurons, hold the potential to assemble into proto-nanobrains. In our study, we investigate the feasibility of utilizing a promising electrochemical technique called differential pulse voltammetry (DPV) to interface with proteinoid nanobrains. We evaluate DPV's suitability by examining critical parameters such as selectivity, sensitivity, and linearity of the electrochemical responses. The research systematically explores the influence of various operational factors, including pulse width, pulse amplitude, scan rate, and scan time. Encouragingly, our findings indicate that DPV exhibits significant potential as an efficient electrochemical interface for proteinoid nanobrains. This technology opens up new avenues for developing artificial neural networks with broad applications across diverse fields of research.

Multi-wavelength diffractive neural network with the weighting method

Recently, the diffractive deep neural network (D2NN) has demonstrated the advantages to achieve large-scale computational tasks in terms of high speed, low power consumption, parallelism, and scalability. A typical D2NN with cascaded diffractive elements is designed for monochromatic illumination. Here, we propose a framework to achieve the multi-wavelength D2NN (MW-D2NN) based on the method of weight coefficients. In training, each wavelength is assigned a specific weighting and their output planes construct the wavelength weighting loss function. The trained MW-D2NN can implement the classification of images of handwritten digits at multi-wavelength incident beams. The designed 3-layers MW-D2NN achieves a simulation classification accuracy of 83.3%. We designed a 1-layer MW-D2NN. The simulation and experiment classification accuracy are 71.4% and 67.5% at RGB wavelengths. Furthermore, the proposed MW-D2NN can be extended to intelligent machine vision systems for multi-wavelength and incoherent illumination.

Convolution Neural Network-Assisted Smart Fluorescent-Tongue Based on Lanthanide Ion-Induced Forming MOF/HOF Composite for Differentiation of Flavor Compounds and Wine Identification

Wine flavor is a vital quality characteristic in wine, influenced by those flavor components with low sensory thresholds. It is crucial to recognize and classify the wine components related to their flavor contribution. The integration of fluorescent sensors and artificial intelligence shows huge potential in flavor recognition by emulation of the gustatory perception system. Meanwhile, achieving information identification of wine based on multiple information barcodes has hopeful applications in anticounterfeiting. In this study, we present a simple method in which organic linkers are weaved into a hydrogen-bonded organic framework (HOF) for the available transformation of a metal-bonded organic framework (MOF) induced by lanthanide ions (Ln3+). The fluorescent Ln-MOF/HOF composite exhibits high sensitivity, rapid response, and good recyclability for detecting seven flavor compounds in wine, including tannic acid, ionone, vanillin, anethole, anisaldehyde, hydroxybenzaldehyde, and 4-hydroxy-2-methylacetophenone. Depending on its satisfactory detectability, a novel strategy is provided in which a fluorescent sensor is able to function as a smart fluorescent-tongue (F-tongue) by the aid of convolutional neural network to differentiate these seven flavor compounds. In addition, the Ln-MOF/HOF composite has been used to prepare multiple information barcodes for wine information identification on the basis of dynamic fluorescence response toward tannic acid. The mimetic gustatory perception system developed in this study may offer a promising strategy for flavor recognition in food and further food anticounterfeiting.

Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system

An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.

Laser nanoprinting of 3D nonlinear holograms beyond 25000 pixels-per-inch for inter-wavelength-band information processing

Nonlinear optics provides a means to bridge between different electromagnetic frequencies, enabling communication between visible, infrared, and terahertz bands through χ(2) and higher-order nonlinear optical processes. However, precisely modulating nonlinear optical waves in 3D space remains a significant challenge, severely limiting the ability to directly manipulate optical information across different wavelength bands. Here, we propose and experimentally demonstrate a three-dimensional (3D) χ(2)-super-pixel hologram with nanometer resolution in lithium niobate crystals, capable of performing advanced processing tasks. In our design, each pixel consists of properly arranged nanodomain structures capable of completely and dynamically manipulating the complex-amplitude of nonlinear waves. Fabricated by femtosecond laser writing, the nonlinear hologram features a pixel diameter of 500 nm and a pixel density of approximately 25000 pixels-per-inch (PPI), reaching far beyond the state of the art. In our experiments, we successfully demonstrate the novel functions of the hologram to process near-infrared (NIR) information at visible wavelengths, including dynamic 3D nonlinear holographic imaging and frequency-up-converted image recognition. Our scheme provides a promising nano-optic platform for high-capacity optical storage and multi-functional information processing across different wavelength ranges.

Low-threshold all-optical nonlinear activation function based on injection locking in distributed feedback laser diodes

We experimentally demonstrate an all-optical nonlinear activation unit based on the injection-locking effect of distributed feedback laser diodes (DFB-LDs). The nonlinear carrier dynamics in the unit generates a low-threshold nonlinear activation function with optimized operating conditions. The unit can operate at a low threshold of -15.86 dBm and a high speed of 1 GHz, making it competitive among existing optical nonlinear activation approaches. We apply the unit to a neural network task of solving the second-order ordinary differential equation. The fitting error is as low as 0.0034, verifying the feasibility of our optical nonlinear activation approach. Given that the large-scale fan-out of optical neural networks (ONNs) will significantly reduce the optical power in one channel, our low-threshold scheme is suitable for the development of high-throughput ONNs.

Ultrafast dynamic machine vision with spatiotemporal photonic computing

Ultrafast dynamic machine vision in the optical domain can provide unprecedented perspectives for high-performance computing. However, owing to the limited degrees of freedom, existing photonic computing approaches rely on the memory's slow read/write operations to implement dynamic processing. Here, we propose a spatiotemporal photonic computing architecture to match the highly parallel spatial computing with high-speed temporal computing and achieve a three-dimensional spatiotemporal plane. A unified training framework is devised to optimize the physical system and the network model. The photonic processing speed of the benchmark video dataset is increased by 40-fold on a space-multiplexed system with 35-fold fewer parameters. A wavelength-multiplexed system realizes all-optical nonlinear computing of dynamic light field with a frame time of 3.57 nanoseconds. The proposed architecture paves the way for ultrafast advanced machine vision free from the limits of memory wall and will find applications in unmanned systems, autonomous driving, ultrafast science, etc.

Arbitrarily rotated optical axis waveguide induced by a trimming line

Rotated optical axis waveguides can facilitate on-chip arbitrary wave-plate operations, which are crucial tools for developing integrated universal quantum computing algorithms. In this paper, we propose a unique technique based on femtosecond laser direct writing technology to fabricate arbitrarily rotated optical axis waveguides. First, a circular isotropic main waveguide with a non-optical axis was fabricated using a beam shaping method. Thereafter, a trimming line was used to create an artificial stress field near the main waveguide to induce a rotated optical axis. Using this technique, we fabricated high-performance half- and quarter-wave plates. Subsequently, high-fidelity (97.1%) Pauli-X gate operation was demonstrated via quantum process tomography, which constitutes the basis for the full manipulation of on-chip polarization-encoded qubits. In the future, this work is expected to lead to new prospects for polarization-encoded information in photonic integrated circuits.

Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks

Microalgae have remarkable potential for wastewater bioremediation since they can efficiently uptake nitrogen and phosphorus in a sustainable and environmentally friendly treatment system. However, wastewater composition greatly depends on its source and has a significant seasonal variability. This study aimed to evaluate the impact of different N:P molar ratios on the growth of Chlorella vulgaris and nutrient removal from synthetic wastewater. Furthermore, artificial neural network (ANN) threshold models, optimised by genetic algorithms (GAs), were used to model biomass productivity (BP) and nitrogen/phosphorus removal rates (RR_N/RR_P). The impact of various inputs culture variables on these parameters was evaluated. Microalgal growth was not nutrient limited since the average biomass productivities and specific growth rates were similar between the experiments. Nutrient removal efficiencies/rates reached 92.0 ± 0.6%/6.15 ± 0.01 mg_N L^-1 d^-1 for nitrogen and 98.2 ± 0.2%/0.92 ± 0.03 mg_P L^-1 d^-1 for phosphorus. Low nitrogen concentration limited phosphorus uptake for low N:P ratios (e.g., 2 and 3, yielding 36 ± 2 mg_DW mg_P^-1 and 39 ± 3 mg_DW mg_P^-1, respectively), while low phosphorus concentration limited nitrogen uptake with high ratios (e.g., 66 and 67, yielding 9.0 ± 0.4 mg_DW mg_N^-1 and 8.8 ± 0.3 mg_DW mg_N^-1, respectively). ANN models showed a high fitting performance, with coefficients of determination of 0.951, 0.800, and 0.793 for BP, RR_N, and RR_P, respectively. In summary, this study demonstrated that microalgae could successfully grow and adapt to N:P molar ratios between 2 and 67, but the nutrient uptake was impacted by these variations, especially for the lowest and highest N:P molar ratios. Furthermore, GA-ANN models demonstrated to be relevant tools for microalgal growth modelling and control. Their high fitting performance in characterising this biological system can contribute to reducing the experimental effort for culture monitoring (human resources and consumables), thus decreasing the costs of microalgae production.

Deep learning-enabled broadband full-Stokes polarimeter with a portable fiber optical spectrometer

Portable fiber optical spectrometers (PFOSs) have been widely used in the contemporary industrial and agricultural production and life due its low cost and small volume. PFOSs mainly combine one fiber to guide light and one optical spectrometer to detect spectra. In this work, we demonstrate that PFOSs can work as a broadband full-Stokes polarimeter through slightly bending the fiber several times and establishing the mapping relationship between the Stokes parameters S^ and the bending-dependent light intensities I^, i.e., S^=f(I^). The different bending geometries bring different birefringence effects and reflection effects that change the polarization state of the out-going light. In the meanwhile, the grating owns a polarization-depended diffraction efficiency especially for the asymmetric illumination geometry that introduces an extrinsic chiroptical effect, which is sensitive to both the linear and spin components of light. The minimum mean squared error (MSE) can reach to smaller than 1% for S₁, S₂, and S₃ at 810 nm, and the averaged MSE in the wave band from 440 nm to 840 nm is smaller than 2.5%, where the working wavelength can be easily extended to arbitrary wave band by applying PFOSs with proper parameters. Our findings provide a convenient and practical method for detecting full-Stokes parameters.

MXene-Nanoflakes-Enabled All-Optical Nonlinear Activation Function for On-Chip Photonic Deep Neural Networks

2D metal carbides and nitrides (MXene) are promising material platforms for on-chip neural networks owing to their nonlinear saturable absorption effect. The localized surface plasmon resonances in metallic MXene nanoflakes may play an important role in enhancing the electromagnetic absorption; however, their contribution is not determined due to the lack of a precise understanding of its localized surface plasmon behavior. Here, a saturable absorber made of MXene thin film and a silicon waveguide with MXene flakes overlayer are developed to perform neuromorphic tasks. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure of the proposed MXene-based activator configurations via tuning the wavelength of operation. The capability and feasibility of the obtained results of machine-learning applications are confirmed via handwritten digit classification task, with near 99% accuracy. These findings can guide the design of advanced ultrathin saturable absorption materials on a chip for a broad range of applications.

Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.

Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human-machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.

Influence of Public Sports Services on Residents' Mental Health at Communities Level: New Insights from China

It is generally believed that sports play an important role in healing and boosting mental health. The provision of public sports services is important for enhancing residents' physical fitness and mental health, and for promoting their satisfaction with government public services. To build and strengthen a high-quality sports service-oriented society, it is important to explore whether community public sports services influence residents' mental health. To explore this phenomenon, the study gathered data from China and employed multi-level regression models to meet the study objective. The results show that the residents' age difference is 0.03, and the average daily exercise time is 0.02, which is significantly correlated with residents' mental health. The results show that the lower the availability and greening of sports facilities, and the fewer rest facilities there are, the higher the mental distress of residents may be. Conversely, the improvement of the greening and availability of sports facilities can facilitate the promotion of residents' mental health levels. Moreover, it was found that the mental health of residents is mainly and positively affected by the cleanliness of sports facilities. The street environment affects mental health and is attributed to the damage to sports facilities. Neighborhood communication also improves residents' mental health, and trust between neighbors has the greatest impact on reducing mental distress. Finally, the study proposes that the government should propose strategies to optimize the provision of community public sports services in the study area to boost both social and mental health benefits.

Design of metasurfaces with decoupled amplitude and phase response for spatial light modulation

Spatial light modulators based on metasurfaces have attracted great attention due to their abilities of amplitude and phase modulation. However, the traditional one degree of freedom (1-DOF) tunable metasurfaces are limited by incomplete phase coverage and coupled amplitude and phase modulation. Here, we propose an optimization method for 2-DOF tunable metasurfaces within the framework of temporal coupled mode theory. As a validation of the proposed method, we present a germanium antimony tellurium (GST)-alloy-based 2-DOF tunable reflective metasurface. Full-wave simulation shows that independent modulation of amplitude and phase is realized with full phase coverage and amplitude range from 0 to 0.55. Our proposed design scheme for a 2-DOF tunable metasurface may facilitate the development of high-performance metasurface devices.

Enhancement of calculation accuracy of the integrated photonic tensor flow processer by global optical power allocation

We present a global optical power allocation architecture, which can enhance the calculation accuracy of the integrated photonic tensor flow processor (PTFP). By adjusting the optical power splitting ratio according to the weight value and loss of each calculating unit, this architecture can efficiently use optical power so that the signal-to-noise ratio of the PTFP is enhanced. In the case of considering the on-chip optical delay line and spectral loss, the calculation accuracy measured in the experiment is enhanced by more than 1 bit compared with the fixed optical power allocation architecture.

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks

Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows for aberration correction, to the imaging of near-transparent biological samples in phase contrast microscopy. Current phase retrieval schemes rely on complex digital algorithms that process data acquired from precise wavefront sensors, reconstructing the optical phase information at great expense of computational resources. Here, we present a compact optical-electronic module based on multi-layered diffractive neural networks printed on imaging sensors, capable of directly retrieving Zernike-based pupil phase distributions from an incident point spread function. We demonstrate this concept numerically and experimentally, showing the direct pupil phase retrieval of superpositions of the first 14 Zernike polynomials. The integrability of the diffractive elements with CMOS sensors shows the potential for the direct extraction of the pupil phase information from a detector module without additional digital post-processing.

Order recognition by Schubert polynomials generated by optical near-field statistics via nanometre-scale photochromism

Irregular spatial distribution of photon transmission through a photochromic crystal photoisomerized by a local optical near-field excitation was previously reported, which manifested complex branching processes via the interplay of material deformation and near-field photon transfer therein. Furthermore, by combining such naturally constructed complex photon transmission with a simple photon detection protocol, Schubert polynomials, the foundation of versatile permutation operations in mathematics, have been generated. In this study, we demonstrated an order recognition algorithm inspired by Schubert calculus using optical near-field statistics via nanometre-scale photochromism. More specifically, by utilizing Schubert polynomials generated via optical near-field patterns, we showed that the order of slot machines with initially unknown reward probability was successfully recognized. We emphasized that, unlike conventional algorithms, the proposed principle does not estimate the reward probabilities but exploits the inversion relations contained in the Schubert polynomials. To quantitatively evaluate the impact of Schubert polynomials generated from an optical near-field pattern, order recognition performances were compared with uniformly distributed and spatially strongly skewed probability distributions, where the optical near-field pattern outperformed the others. We found that the number of singularities contained in Schubert polynomials and that of the given problem or considered environment exhibited a clear correspondence, indicating that superior order recognition is attained when the singularity of the given situations is presupposed. This study paves way for physical computing through the interplay of complex natural processes and mathematical insights gained by Schubert calculus.

Multiscale diffractive U-Net: a robust all-optical deep learning framework modeled with sampling and skip connections

As an all-optical learning framework, diffractive deep neural networks (D²NNs) have great potential in running speed, data throughput, and energy consumption. The depth of networks and the misalignment of layers are two problems to limit its further development. In this work, a robust all-optical network framework (multiscale diffractive U-Net, MDUNet) based on multi-scale features fusion has been proposed. The depth expansion and alignment robustness of the network can be significantly improved by introducing sampling and skip connections. Compared with common all-optical learning frameworks, MDUNet achieves the highest accuracy of 98.81% and 89.11% on MNIST and Fashion-MNIST respectively. The testing accuracy of MNIST and Fashion-MNIST can be further improved to 99.06% and 89.86% respectively by using the ensemble learning method to construct the optoelectronic hybrid neural network.

Integratable electro-optic modulator based on a polymer-embedded silicon racetrack resonator with high electro-optic wavelength tuning

Electro-optic (EO) modulators based on polymer-embedded silicon racetrack resonators (EOM-PSRR) are investigated. To obtain the single-mode propagation condition, the mode and transmission characteristics of the polymer-embedded silicon waveguide are simulated by the finite element method (FEM). By adding a static bias voltage, the EO modulation performances of EOM-PSRR embedded with lithium niobate (LiNbO₃), EO polymer (AJ309), and hybrid EO polymer/TiO₂ material (HEOT) are studied. The results show that the EOM-PSRR embedded with LiNbO₃ achieves a high modulation depth (MD) of ∼27.6dB with a low EO wavelength tuning (λ_EO) of 10 pm/V. However, the EOM-PSRR embedded with HEOT has a high λ_EO of 100 pm/V but a low MD of ∼6.2dB with an extinction ratio of ∼5.2dB. The EOM-PSRR has potential application prospects in optical communication, optical signal processing, and optical network links. It can be produced as an optical frequency comb generator in a dense wavelength division multiplexing system, an EO frequency shifter for laser beams, an optical soliton former, and a photon time-delay device in a phased array radar.

Broad-Band Ultrafast All-Optical Switching Based on Enhanced Nonlinear Absorption in Corrugated Indium Tin Oxide Films

Ultrafast all-optical switches based on epsilon-near-zero (ENZ)-enhanced nonlinear refraction in transparent conducting oxides have achieved exciting results in realizing large absolute modulations. However, broad-band, polarization-independent, and wide-angle ultrafast all-optical switches have been challenging to produce, due to the inherent narrow band, polarization-dependent, and angle-dependent characteristics of the ENZ effect. To this end, we propose an ultrafast all-optical switch based on the enhanced nonlinear absorption of corrugated indium tin oxide (ITO) thin films. Taking advantage of the perfect absorption and localized field enhancement of the ENZ and localized surface plasmon resonance modes, we significantly enhanced the nonlinear absorption of the corrugated ITO film in the 1450-1650 nm telecom band. The experimental results show that the nonlinear saturable absorption coefficient of the corrugated ITO film at 1450 nm was as high as -1.5 × 10⁵ cm GW^-1, enabling all-optical switching to obtain an extinction ratio of 14.32 dB and an ultrafast switching time of 350 fs at a pump fluence of 18.51 mJ cm^-2. Furthermore, the all-optical switch achieved an extinction ratio of over 15 dB and an insertion loss of approximately 2.6 dB within the 200 nm absorption band and exhibited polarization-independent and wide-angle features. The ultrafast temporal response can be attributed to intraband transient bleaching of the corrugated ITO film. Our findings demonstrate that corrugated ENZ films can overcome the inherent narrow-band, polarization-dependent, and angle-dependent problems of natural ENZ materials without increasing the response time, making them a potential ENZ ultrafast all-optical switching material platform.

A multichannel optical computing architecture for advanced machine vision

Endowed with the superior computing speed and energy efficiency, optical neural networks (ONNs) have attracted ever-growing attention in recent years. Existing optical computing architectures are mainly single-channel due to the lack of advanced optical connection and interaction operators, solving simple tasks such as hand-written digit classification, saliency detection, etc. The limited computing capacity and scalability of single-channel ONNs restrict the optical implementation of advanced machine vision. Herein, we develop Monet: a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework where the inter- and intra- channel connections are mapped to optical interference and diffraction. In our Monet, optical interference patterns are generated by projecting and interfering the multichannel inputs in a shared domain. These patterns encoding the correspondences together with feature embeddings are iteratively produced through the projection-interference process to predict the final output optically. For the first time, Monet validates that multichannel processing properties can be optically implemented with high-efficiency, enabling real-world intelligent multichannel-processing tasks solved via optical computing, including 3D/motion detections. Extensive experiments on different scenarios demonstrate the effectiveness of Monet in handling advanced machine vision tasks with comparative accuracy as the electronic counterparts yet achieving a ten-fold improvement in computing efficiency. For intelligent computing, the trends of dealing with real-world advanced tasks are irreversible. Breaking the capacity and scalability limitations of single-channel ONN and further exploring the multichannel processing potential of wave optics, we anticipate that the proposed technique will accelerate the development of more powerful optical AI as critical support for modern advanced machine vision.

LOEN: Lensless opto-electronic neural network empowered machine vision

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.

Photonics enabled intelligence system to identify SARS-CoV 2 mutations

Abstract

The COVID-19, MERS-CoV, and SARS-CoV are hazardous epidemics that have resulted in many deaths which caused a worldwide debate. Despite control efforts, SARS-CoV-2 continues to spread, and the fast spread of this highly infectious illness has posed a grave threat to global health. The effect of the SARS-CoV-2 mutation, on the other hand, has been characterized by worrying variations that modify viral characteristics in response to the changing resistance profile of the human population. The repeated transmission of virus mutation indicates that epidemics are likely to occur. Therefore, an early identification system of ongoing mutations of SARS-CoV-2 will provide essential insights for planning and avoiding future outbreaks. This article discussed the following highlights: First, comparing the omicron mutation with other variants; second, analysis and evaluation of the spread rate of the SARS-CoV 2 variations in the countries; third, identification of mutation areas in spike protein; and fourth, it discussed the photonics approaches enabled with artificial intelligence. Therefore, our goal is to identify the SARS-CoV 2 virus directly without the need for sample preparation or molecular amplification procedures. Furthermore, by connecting through the optical network, the COVID-19 test becomes a component of the Internet of healthcare things to improve precision, service efficiency, and flexibility and provide greater availability for the evaluation of the general population.

Key points

• A proposed framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations.

• Comparative scatter rates Omicron variant and other SARS-CoV 2 variations per country.

• Evaluating mutation areas in spike protein and AI enabled by photonic technologies for SARS-CoV 2 virus detection.

Photonic matrix multiplication lights up photonic accelerator 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.

Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows

Manipulation of nano-objects at the microscale is of great technological importance for constructing new functional materials, manipulating tiny amounts of fluids, reconfiguring sensor systems, or detecting tiny concentrations of analytes in medical screening. Here, we show that hydrodynamic boundary flows enable the trapping and manipulation of nano-objects near surfaces. We trigger thermo-osmotic flows by modulating the van der Waals and double layer interactions at a gold-liquid interface with optically generated local temperature fields. The hydrodynamic flows, attractive van der Waals and repulsive double layer forces acting on the suspended nanoparticles enable precise nanoparticle positioning and guidance. A rapid multiplexing of flow fields permits the parallel manipulation of many nano-objects and the generation of complex flow fields. Our findings have direct implications for the field of plasmonic nanotweezers and other thermo-plasmonic trapping systems, paving the way for nanoscopic manipulation with boundary flows.

The manipulation of nano-objects in liquid environments is relevant for sensor systems, chemical design, and screening in medical applications. The authors propose an approach to manipulate nano-objects based on nanoscale hydrodynamic boundary flows induced by optical heat generation.

A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

Neuron-Inspired Steiner Tree Networks for 3D Low-Density Metastructures

Three-dimensional (3D) micro-and nanostructures have played an important role in topological photonics, microfluidics, acoustic, and mechanical engineering. Incorporating biomimetic geometries into the design of metastructures has created low-density metamaterials with extraordinary physical and photonic properties. However, the use of surface-based biomimetic geometries restricts the freedom to tune the relative density, mechanical strength, and topological phase. The Steiner tree method inspired by the feature of the shortest connection distance in biological neural networks is applied, to create 3D metastructures and, through two-photon nanolithography, neuron-inspired 3D structures with nanoscale features are successfully achieved. Two solutions are presented to the 3D Steiner tree problem: the Steiner tree networks (STNs) and the twisted Steiner tree networks (T-STNs). STNs and T-STNs possess a lower density than surface-based metamaterials and that T-STNs have Young's modulus enhanced by 20% than the STNs. Through the analysis of the space groups and symmetries, a topological nontrivial Dirac-like conical dispersion in the T-STNs is predicted, and the results are based on calculations with true predictive power and readily realizable from microwave to optical frequencies. The neuron-inspired 3D metastructures opens a new space for designing low-density metamaterials and topological photonics with extraordinary properties triggered by a twisting degree-of-freedom.

Self-organized phase-transition lithography for all-inorganic photonic textures

Realizing general processing applicable to various materials by one basic tool has long been considered a distant dream. Fortunately, ultrafast laser-matter interaction has emerged as a highly universal platform with unprecedented optical phenomena and provided implementation paths for advanced manufacturing with novel functionalities. Here, we report the establishment of a three-dimensional (3D) focal-area interference field actively induced by a single ultrafast laser in transparent dielectrics. Relying on this, we demonstrate a radically new approach of self-organized phase-transition lithography (SOPTL) to achieve super-resolution construction of embedded all-inorganic photonic textures with extremely high efficiency. The generated textures exhibit a tunable photonic bandgap (PBG) in a wide range from ~1.3 to ~2 μm. More complicated interlaced textures with adjustable structural features can be fabricated within a few seconds, which is not attainable with any other conventional techniques. Evidence suggests that the SOPTL is extendable to more than one material system. This study augments light-matter interaction physics, offers a promising approach for constructing robust photonic devices, and opens up a new research direction in advanced lithography.

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

Multichannel Electron Transmission and Fluorescence Resonance Energy Transfer in In2S3/Au/rGO Composite for CO2 Photoreduction

Efficient electron transmission is an important step in the process of CO₂ photoreduction. In this paper, a multi-interface-contacted In₂S₃/Au/reduced graphene oxide (rGO) photocatalyst with the fluorescence resonance energy transfer (FRET) mechanism has been successfully prepared by the solvothermal, self-assembly, and hydrothermal reduction processes. Photocatalytic CO₂ reduction experiments showed that the In₂S₃/Au/rGO (IAr-3) composite exhibited excellent photoreduction performance and photocatalytic stability. The yields of CO and CH₄ obtained after the photoreduction process with IAr-3 as the catalyst were around 4 and 6 times higher than those of pure In₂S₃, respectively. Photoelectrochemical analysis showed that the multi-interface contact and FRET mechanism greatly improved the generation, transmission, and separation efficiency of carriers photogenerated within the photocatalyst. In situ FTIR test was applied to analyze the photocatalytic CO₂ reduction process. ¹³C isotope tracer test confirmed that the carbon source of CO and CH₄ was the CO₂ molecules in the photoreduction process rather than the decomposition of catalyst or TEOA. A potential enhanced photocatalytic mechanism has been discussed in total.

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 nm1,2, 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.

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.

Decoding Optical Data with Machine Learning

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

A Pearl Spectrometer

Information recovery from incomplete measurements, typically performed by a numerical means, is beneficial in a variety of classical and quantum signal processing. Random and sparse sampling with nanophotonic and light scattering approaches has received attention to overcome the hardware limitations of conventional spectrometers and hyperspectral imagers but requires high-precision nanofabrications and bulky media. We report a simple spectral information processing scheme in which light transport through an Anderson-localized medium serves as an entropy source for compressive sampling directly in the frequency domain. As implied by the "lustrous" reflection originating from the exquisite multilayered nanostructures, a pearl (or mother-of-pearl) allows us to exploit the spatial and spectral intensity fluctuations originating from strong light localization for extracting salient spectral information with a compact and thin form factor. Pearl-inspired light localization in low-dimensional structures can offer an alternative of spectral information processing by hybridizing digital and physical properties at a material level.

Artificial intelligence for photonics and photonic materials

Artificial intelligence (AI) is the most important new methodology in scientific research since the adoption of quantum mechanics and it is providing exciting results in numerous fields of science and technology. In this review we summarize research and discuss future opportunities for AI in the domains of photonics, nanophotonics, plasmonics and photonic materials discovery, including metamaterials.

Integrated photonic neural network based on silicon metalines

An integrated photonic neural network is proposed based on on-chip cascaded one-dimensional (1D) metasurfaces. High-contrast transmitarray metasurfaces, termed as metalines in this paper, are defined sequentially in the silicon-on-insulator substrate with a distance much larger than the operation wavelength. Matrix-vector multiplications can be accomplished in parallel and with low energy consumption due to intrinsic parallelism and low-loss of silicon metalines. The proposed on-chip whole-passive fully-optical meta-neural-network is very compact and works at the speed of light, with very low energy consumption. Various complex functions that are performed by digital neural networks can be implemented by our proposal at the wavelength of 1.55 µm. As an example, the performance of our optical neural network is benchmarked on the prototypical machine learning task of classification of handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, and an accuracy comparable to the state of the art is achieved.