In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Quantifying fluorescence contrast of latent fingerprints developed with micro/nanoparticles through spectroscopic analysis

Fluorescent micro/nanoparticles have been used to visualize latent fingerprints in forensic science. However, due to the current lack of a systematic method to quantify the fluorescence contrast of developed fingerprints, the comparison of fingerprint visualization effects of various developing agents is still limited to empirical evaluation. Here, we presented a fingerprint fluorescence contrast quantification strategy that comprehensively considers fluorescence intensity and color indexes, based on photoluminescence spectroscopy analysis. The intensity index represents the relative fluorescence intensity (I) between the fingerprint and the background. The color index is quantified by its three elements, including hue, saturation, and value (L), where hue and saturation are reflected by chroma (C). Fluorescence contrast is defined as the product of relative fluorescence intensity, chroma parameter, and the common logarithm of value index (I·C·lg L). The proposed approach quantified fingerprints deposited on substrates with varying degrees of patterning and fluorescence interference, using full-color-emitting micro/nanoparticles, and was compared with existing methods, demonstrating its high detection limit, accuracy, and sensitivity. Finally, we developed a tunable multi-color fingerprint enhancement technique to enhance low-quality and low-contrast fingerprints. Therefore, the established comprehensive fluorescence contrast quantification and enhancement strategy provides a new avenue for practical forensic analysis and identification.

Artificial Axon with Dendritic-like Plasticity by Biomimetic Interface Engineering of Anisotropic Two-Dimensional Tellurium

Spiking neural network (SNN) hardware relies on implicit assumptions that prioritize dendritic/synaptic learning above axon/synaptic concerns, compromising performances in signal capacity, accuracy, and compactness of SNN systems. Herein, we develop an artificial axon by utilizing the heterogeneity and interface state tunability in anisotropic two-dimensional (2D) tellurium (Te). By operating a multiterminal axon under the bioelectricity level, the device achieved neuron-like heterogeneous axon dynamics expansion (∼258%). An excellent dendritic-like tunability (∼197%) exhibits gain on the axons. The synergistic axon-dendrite optimization device exhibits 5-bit programmable conductance, signal filtering, and input enhancing. The accuracy of recognizing data sets based on the SNN algorithm demonstrates efficient optimization (5.2% higher accuracy) of networks by the device features, especially in the case of performing image preprocessing. This artificial neuron solution with anisotropic 2D materials utilizing biomimetic interface engineering provides a universal strategy for compact, high-precision parallel architecture of SNN hardware.

Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human-machine interfaces, marking a pivotal advancement in edge computing and AI deployment.

Photonic Modulation of Negative Differential Transconductance for Tunable Multivalued Logic in Heterojunction Transistors

Logic conversion within multivalued logic (MVL) circuits is a promising solution that enhances complex data processing achieved through the modulation of negative differential transconductance (NDT) characteristics of heterojunction transistors (HTRs) using various external control factors. Among these factors, utilizing photonic modulation for logic switching facilitates multi-state operations, enables wavelength-selective logic conversion and, on this basis, enhances the flexibility of the circuit system. Herein, the implementation of logic conversion in HTRs through the photonic modulation of NDT characteristics is presented. Logic switching in the In2O3/PDPP3T HTR is enabled by tuning its NDT characteristics utilizing optical stimulation with LED light at a specific wavelength. Moreover, operating the HTR through photonic control reduces process requirements while ensuring stable operation, thereby enhancing compatibility and scalability in circuit design. The operating mechanism of the device is also investigated by individually analyzing the carrier flows in two paths and examining the electrical characteristics under dark and illuminated states. To verify the functionality of the MVL circuit, stable switching between binary and ternary logic inverters as a practical application is demonstrated.

Optogenetics-Inspired Nanofluidic Artificial Dendrite with Spatiotemporal Integration Functions

Dendrites play an essential role in processing functions by facilitating the integration of spatial and temporal information in biological system. Nanofluidic memristors, which harness ions for signal transmission within electrolyte solutions, closely resemble biological neuronal ion channels and hold the potential for the development of biorealistic neuromorphic devices. Herein, inspired by the optogenetic technique that utilized light to tune the ions dynamic, an optical-controlled nanofluidic artificial dendrite by embedding layered graphene oxide (GO) within a polydimethylsiloxane (PDMS) elastomer is developed. Taking advantage of the confinement effect of ions in the nanochannel, it has demonstrated optically-modulated ionic currents, which can effectively replicate dendritic functions. The mechanism can be attributed to the migration of Na+ ions, driven by the electric potential difference light illumination. The dendritic spatial and temporal multiport integrations are realized, including the dendritic sublinear/superlinear integrations and spike-rate-dependent plasticity (SRDP). Moreover, the hand withdrawal reflex, as a crucial mode of neuroregulation governed by central nerve and brain control signals, is replicated in the nanofluidic dendrite-based neuromorphic system, capable of managing a range of withdrawal states of a mechanical arm. This work offers a new strategy for developing nanofluidic artificial dendrite and paves the way toward developing advanced neuromorphic sensorimotor systems.

Light-induced tunable threshold voltage and synaptic behavior of a solution-processed indium oxide thin film transistor for logic computing and image denoising

Oxygen vacancies (VO) play a crucial role in promising amorphous metal oxide films for next-generation logic and synaptic computing. Here, a simple and reversible annealing-illumination method is introduced to control the concentration of VO in solution-processed amorphous indium oxide thin-film transistors (TFTs), resulting in the precise regulation of the threshold voltage (VTH) in a large range from 1.6 V to -21.7 V. Meanwhile, clear photo-synaptic behaviors are observed. These impressive behaviors result from the VO-related carrier trapping and detrapping processes. With the precise regulation of VTH by illumination, the TFTs are constructed as inverters, displaying tunable voltage gains from 5.7 to 10.6. Owing to the excellent photo-synaptic behavior, the TFTs are employed to demonstrate the optoelectronic logic functions of "OR", "AND", "NOR", and "NAND". Moreover, a 5 × 5 TFTs array is employed to demonstrate the real-time image preprocessing and image denoising functions, displaying an impressive accuracy of 96%. Furthermore, the improvement of the recognition accuracy will increase to a maximum value of 88%. This work shows the potential of amorphous indium oxide TFTs in future multifunctional logic circuits and efficient, all-optical neuromorphic vision systems.

Polarity-tunable dye-sensitized optoelectronic artificial synapses for physical reservoir computing-based machine vision

Conventional machine vision systems process huge time-series data per second, presenting significant challenges for edge-device applications due to limitations in data transfer and storage. Inspired by the human visual system, artificial optoelectronic synapses replicating synaptic responses have emerged as promising solutions. However, achieving color recognition comparable to human vision remains challenging. Moreover, most optoelectronic artificial synapses rely on photocurrent-based operation, producing low current values and necessitating external circuits. This study reports a self-powered optoelectronic artificial synapse capable of distinguishing wavelengths with a resolution of 10 nm by integrating dye-sensitized solar cells. The device exhibits synaptic responses to light pulses and bipolar responses when exposed to different wavelengths. The wavelength-dependent bipolar behavior enables exceptional separation capabilities, achieving six-bit resolution with 64 distinct states and supporting multiple logic operations, including AND, OR, and XOR, within a single device. Additionally, the device leverages distinct responses to red, green, and blue light irradiation for physical reservoir computing, facilitating the classification of color-coded human motion with an accuracy of 82%. These findings advance the development of optoelectronic artificial synapses for precise, human-eye-like color discrimination.

A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing

With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain's functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the application of nanowire materials (and devices) in neuromorphic computing simulations: First, it introduces the synthesis and preparation methods of nanowire materials. Then, it analyzes in detail the key role of nanowire devices in constructing artificial neural networks, especially their advantages in simulating the functions of neurons and synapses. Compared with traditional silicon-based material devices, it focuses on how nanowire devices can achieve higher connection density and lower energy consumption, thereby enabling new types of neuromorphic computing. Finally, it looks forward to the application potential of nanowire devices in the field of future neuromorphic computing, expecting them to become a key force in promoting the development of intelligent computing, with extensive application prospects in the fields of informatics and medicine.

Versatile optoelectronic memristor based on wide-bandgap Ga2O3 for artificial synapses and neuromorphic computing

Optoelectronic memristors possess capabilities of data storage and mimicking human visual perception. They hold great promise in neuromorphic visual systems (NVs). This study introduces the amorphous wide-bandgap Ga2O3 photoelectric synaptic memristor, which achieves 3-bit data storage through the adjustment of current compliance (Icc) and the utilization of variable ultraviolet (UV-254 nm) light intensities. The "AND" and "OR" logic gates in memristor-aided logic (MAGIC) are implemented by utilizing voltage polarity and UV light as input signals. The device also exhibits highly stable synaptic characteristics such as paired-pulse facilitation (PPF), spike-intensity dependent plasticity (SIDP), spike-number dependent plasticity (SNDP), spike-time dependent plasticity (STDP), spike-frequency dependent plasticity (SFDP) and the learning experience behavior. Finally, when integrated into an artificial neural network (ANN), the Ag/Ga2O3/Pt memristive device mimicked optical pulse potentiation and electrical pulse depression with high pattern accuracy (90.7%). The single memristive cells with multifunctional features are promising candidates for optoelectronic memory storage, neuromorphic computing, and artificial visual perception applications.

Smart touchless palm sensing via palm adjustment and dynamic registration

Touchless palm recognition is increasingly popular for its effectiveness, privacy, and hygiene benefits in biometric systems. However, several challenges remain, including significant performance degradation caused by variations in palm positioning and capture distance. To address these issues, this paper introduces a comprehensive sensing system that integrates dynamic registration with robust palm adjustment. Specifically, we conduct a thorough investigation of distance variations to establish optimal registration settings. In addition, we propose an edge-aware, rotation-invariant region of interest alignment method, which ensures spatial alignment for any given palm across its different samples, even under challenging conditions. By embedding it into a palm registration framework based on video sequences, we improve the system's ability to adapt to varying conditions automatically. Extensive experiments on various datasets demonstrate that the proposed method significantly enhances the performance of touchless palm recognition systems.

Daylight Visualization of Latent Fingerprints Exceeding Level 3 Details through Contradictory Electrostatic and Hydrogen-Bonding Interactions

Daylight visualization of latent fingerprints (LFPs) is highly desirable due to its convenience and safety for inspectors. However, achieving this visualization with details exceeding level 3 using nontoxic materials presents a significant challenge. Here, we address this challenge by utilizing cyclosiloxane-linked fluorescent porous polymers (FPPs) as the developers. These FPPs were synthesized from 2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane and brominated monomers via Heck reactions. The resulting FPPs, available as powders in various colors, exhibit high porosity and strong fluorescence. By employing the classical powder dusting method with FPPs, LFPs with level 1-3 details become visible on various substrates, including copper, iron, aluminum, tinfoil, and wood, under daylight. This approach provides high resolution and contrast with strong resistance to background interference on colored substrates. The efficient visualization is attributed to contradictory electrostatic and hydrogen-bonding interactions between FPPs and fingerprint secretions, which ensure strong adhesion of the materials exclusively to the ridges of fingerprints rather than the furrows. Furthermore, the FPPs are noncytotoxic and do not impede DNA extraction and identification after LFP visualization. This study represents the first example of daylight visualization of LFPs with details surpassing level 3 using nontoxic developers, thereby mitigating the risks associated with material toxicity and light exposure for inspectors. These findings highlight the potential of FPPs as efficient developers for LFP visualization in crime scenes. This straightforward and broadly applicable protocol may pave the way for further advancements in daylight visualization methods for LFPs.

Flexible Alternating-Current Electroluminescent Devices for Reliable Identification of Fingerprints

Flexible bioelectronic devices, which can directly detect various external stimuli or biosignals and communicate the information to the users, have been broadly investigated due to the increasing demand for wearable devices. Among them, alternating-current electroluminescence (ACEL) devices are proposed as sensitive sensing systems for various targets, such as fingerprints. Herein, we propose a method for preparing high-performance ACEL devices by using an Ag electrode, polyethylene terephthalate (PET) substrate, FKM/EMI ionogel, and ZnS:Cu/BaTiO3/Ecoflex emissive layer. Their influence has also been studied for achieving high performances. The results demonstrate that the prepared ACEL devices can achieve high performances of emitting bright green and blue light when contacted with various ionic liquids. Significantly, they achieved good sensing performance for detecting Na+ with a limit of detection at 17.1 μM in the linear range of 100-800 mM. Moreover, the ACEL devices can be used for identity recognition, as they are capable of efficient collection and distinguishing of fingerprints. Even the characteristics of fingerprints collected from bending surfaces or contaminated fingers could be distinguished by the naked eyes. Compared with commercial fingerprint devices, our ACEL devices exhibit superior performance in fingerprint identification. High-resolution and three-dimensional image analysis further validates the reliability of our ACEL devices in fingerprint collection and identification. As such, we believe that the designed ACEL devices have very promising application prospects in many fields.

Ion-Gel Gated Perovskite Field-Effect Transistors with Low Power Consumption and High Stability for Neuromorphic Applications

Ruddlesden-Popper (RP) tin halide perovskites (THPs), exemplified by PEA2SnI4, are promising two-dimensional semiconductors for optoelectronic applications, yet their field-effect transistors (FETs) often suffer from high operating voltages and stability issues. Addressing these challenges, we developed a novel approach for integrating ion gel dielectrics composed of PVDF-HFP and [EMIM+][TFSI-] with PEA2SnI4, achieving FETs with record-low operating voltages as low as 2 V. Additionally, by substituting PEA+ with BA+ in BA2SnI4 FETs, we achieve enhanced device stability, with these devices exhibiting prolonged functionality exceeding 100 days. Uniform performance was also observed across 30 randomly tested devices fabricated on a 2 inch silicon wafer. These FETs also demonstrated synaptic behavior with ultralow energy consumption (5 × 10-11 J per operation). Leveraging these advancements, we constructed artificial neural networks for item classification, achieving high accuracy (97%). Moreover, the energy consumption for a single training process based on ion-gel gated BA2SnI4 FETs is approximately 2 orders of magnitude lower than that of similar networks based on the NVIDIA GeForce RTX 4060 GPU. Our results highlight the potential of ion-gel gated BA2SnI4 FETs for low-power, high-stability applications, paving the way for the next generation of perovskite-based electronic and neuromorphic devices.

An Analysis of Components and Enhancement Strategies for Advancing Memristive Neural Networks

Advancements in artificial intelligence (AI) and big data have highlighted the limitations of traditional von Neumann architectures, such as excessive power consumption and limited performance improvement with increasing parameter numbers. These challenges are significant for edge devices requiring higher energy and area efficiency. Recently, many reports on memristor-based neural networks (Mem-NN) using resistive switching memory have shown efficient computing performance with a low power requirement. Even further performance optimization can be made using engineering resistive switching mechanisms. Nevertheless, systematic reviews that address the circuit-to-material aspects of Mem-NNs, including their dedicated algorithms, remain limited. This review first categorizes the memristor-based neural networks into three components: pre-processing units, processing units, and learning algorithms. Then, the optimization methods to improve integration and operational reliability are discussed across materials, devices, circuits, and algorithms for each component. Furthermore, the review compares recent advancements in chip-level neuromorphic hardware with conventional systems, including graphic processing units. The ongoing challenges and future directions in the field are discussed, highlighting the research to enhance the functionality and reliability of Mem-NNs.

Bias-Switchable Photodetection and Photosynapse Dual-Functional Devices Based on 2D Perovskite/Organic Heterojunction for Imaging-to-Recognition Conversion

Optoelectronic devices with imaging and recognition capabilities are crucial for developing artificial visual system (AVS). Bias-switchable photodetection and photosynaptic devices have been developed using 2D perovskite oxide/organic heterojunctions. This unique structure allows for modulated carrier dynamics under varied bias conditions, enabling the devices to function as photodetectors without bias and as photosynapses with bias. At zero bias, the device achieves high responsivity (≈0.36 A W-1 at 320 nm) and rapid response speed (0.57 s). Under a -0.5 V bias, it exhibits persistent photoconductivity (PPC), resulting in neuromorphic synaptic behaviors with a paired-pulse facilitation (PPF) index exceeding 300%. Moreover, an 8 × 8 sensor array demonstrates image sensing and memory capabilities, showing in situ enhanced imaging when switching the bias from 0 to -0.5 V, and over 200 s of image memory. The image processing and recognition abilities are further explored by constructing an AVS using a 28 × 28 device array combined with an artificial neural network (ANN). The adjustable synaptic weight under different reverse biases allowed for optimized simulated recognition, achieving an accuracy of 92% after 160 training epochs. This work presents a novel method for creating dual-functional photodetection and photosynaptic devices, paving the way for a more integrated and efficient AVS.

Memristive Ion Dynamics to Enable Biorealistic Computing

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention. Ion-based memristive devices (IMDs), owing to the intrinsic functional similarities to their biological counterparts, hold significant promise for implementing emerging neuromorphic learning and computing algorithms. In this article, we review the fundamental mechanisms of IMDs based on ion drift and diffusion to elucidate the origins of their diverse dynamics. We then examine how these mechanisms operate within different materials to enable IMDs with various types of switching behaviors, leading to a wide range of applications, from emulating biological components to realizing specialized computing requirements. Furthermore, we explore the potential for IMDs to be modified and tuned to achieve customized dynamics, which positions them as one of the most promising hardware candidates for executing bioinspired algorithms with unique specifications. Finally, we identify the challenges currently facing IMDs that hinder their widespread usage and highlight emerging research directions that could significantly benefit from incorporating IMDs.

Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing

Physical reservoir computing (PRC) using synaptic devices has attracted attention as a promising edge artificial intelligence device. To handle time-series data on various time scales, it is necessary to fabricate devices with the desired time scale. In this study, we fabricated a dye-sensitized solar-cell-based synaptic device with controllable time constants by changing the light intensity. This device showed synaptic features, such as paired-pulse facilitation and paired-pulse depression, in response to light intensity. Moreover, we found that the high computational performance of the time-series data processing task was achieved by changing the light intensity, even when the input pulse width was varied. In addition, the fabricated device can be used for motion recognition tasks. This study paves the way for realizing multiple time-scale PRC.

Optoelectronic array of photodiodes integrated with RRAMs for energy-efficient in-sensor computing

The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

Rotor proliferation promotes high-brightness AIE of iridium emitter accomplishing high-contrast luminous imaging of latent fingerprints to level 3 details

Luminous imaging of latent fingerprints (LFPs) necessitates the possession of high-brightness aggregation-state luminescence by developers to ensure sufficient imaging contrast and resolution. A novel strategy involving incremental rotor modification is presented for AIE activation of the iridium developer. The rotor proliferation prominently improves the rotational activity of groups and facilitates high-efficiency RIM, thereby prompting the AIE activation of iridium developer with high luminous efficiency. Subsequently, a prompt, high-contrast, and robust LFP imaging protocol is developed utilizing the high-brightness AIE-active iridium developer. This innovative protocol realizes the luminous imaging and quantification of microscopic features in fingerprint ridges and furrows, including ridge widths, edge morphology of ridges, included angles, pores, and pore pitches with exceptional imaging contrast and refined detail resolution. Moreover, it allows for accurate identification of individual traits across diverse substrates without any pre-/post-processing to LFPs. The high-brightness AIE-active iridium developer provides outstanding aging resistance to developed fingerprints, thereby strongly supporting the acquisition, transfer, and preservation of fingerprint evidence. The luminous imaging protocol of LFPs based on high-brightness AIE exhibits robust adaptability to actual scenes and offers a premium scheme for facilitating forensic investigation.

Large-scale high uniform optoelectronic synapses array for artificial visual neural network

Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm2, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.

Interface-engineered non-volatile visible-blind photodetector for in-sensor computing

Ultraviolet (UV) detection is extensively used in a variety of applications. However, the storage and processing of information after detection require multiple components, resulting in increased energy consumption and data transmission latency. In this paper, a reconfigurable UV photodetector based on CeO2/SrTiO3 heterostructures is demonstrated with in-sensor computing capabilities achieved through interface engineering. We show that the non-volatile storage capability of the device could be significantly improved by the introduction of an oxygen reservoir. A photodetector array operated as a single-layer neural network was constructed, in which edge detection and pattern recognition were realized without the need for external memory and computing units. The location and classification of corona discharges in real-world environments were also simulated and achieved an accuracy of 100%. The approach proposed here offers promising avenues and material options for creating non-volatile smart photodetectors.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.

Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal-oxide-semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications.

Catalyst-Free Polymorphic β-Ga2O3 Nanomaterials for Solar-Blind Optoelectronic Devices: Applications in Imaging and Neural Communication

The continuous advancements in ultraviolet-C (UV-C) optoelectronics are poised to meet the growing demand for efficient and innovative optoelectronic devices, particularly in image sensing and neural communication. This study proposes a low-cost tube sealing and muffle calcination process for the catalyst-free synthesis of polymorphic β-Ga2O3 nanomaterials. These nanomaterials are synthesized via a vapor-solid (VS) growth mechanism, enabling the formation of high-quality nanowires (NWs), nanobelts (NBs), and nanosheets (NSs). UV-C photodetectors (PDs) fabricated with β-Ga2O3 nanobelts demonstrated exceptional performance, exhibiting a responsivity of 4.62 × 105 A W-1 and a specific detectivity of 4.78 × 1012 Jones under 254 nm light. This PD enabled high-sensitivity and high-contrast UV-C imaging, effectively capturing the letters "CNU" and a "Panda" pattern. Additionally, the β-Ga2O3 nanowire-based optoelectronic synapse (OES) device displayed efficient light sensing and significant persistent photoconductivity, accurately mimicking synaptic behaviors such as short-term to long-term memory transitions and memory reinforcement. The OES device is successfully integrated into a wireless optical communication system, effectively simulating neural signal transmission by outputting the current waveform signal of "CNU 1954" and exhibiting notable UV-C light sensing and learning abilities. This work not only introduces a method for synthesizing polymorphic β-Ga2O3 nanomaterials but also underscores their potential in advanced UV-C optoelectronic applications, including image sensing and neural communication.

Flexible Printed Ultraviolet-to-Near-Infrared Broadband Optoelectronic Carbon Nanotube Synaptic Transistors for Fast and Energy-Efficient Neuromorphic Vision Systems

To simulate biological visual systems and surpass their functions and performance, it is essential to develop high-performance optoelectronic neuromorphic electronics with broadband response, low power consumption, and fast response speed. Among these, optoelectronic synaptic transistors have emerged as promising candidates for constructing neuromorphic visual systems. In this work, flexible printed broadband (from 275 to 1050 nm) optoelectronic carbon nanotube synaptic transistors with good stability, high response speed (3.14 ms), and low-power consumption (as low as 0.1 fJ per event with the 1050 nm pulse illumination) using PbS quantum dots (QDs) modified semiconducting single-walled carbon nanotubes (sc-SWCNTs) as active layers are developed. In response to optical pulses within the ultraviolet to near-infrared wavelength range, the optoelectronic neuromorphic devices exhibit excitatory postsynaptic current, paired-pulse facilitation, and a transition from short-term plasticity to long-term plasticity, and other optical synaptic behaviors. Furthermore, a simplified neural morphology visual array is developed to simulate integrated functions such as image perception, memory, and preprocessing. More importantly, it can also emulate other complicated bionic functions, such as the infrared perception of salmon eyes and the warning behavior of reindeer in different environments. This work holds immense significance in advancing the development of artificial neural visual systems.

Physical Reservoir Computing Using Tellurium-Based Gate-Tunable Artificial Photonic Synapses

We report tellurium (Te) thin-film-based artificial photonic synapses and their application to physical reservoir computing (PRC). The Te-based artificial photonic synapses were fabricated by using sputtered Te thin films and spray-coated MXene (Ti3C2) electrodes. A thorough investigation of the field-dependent persistent photoconductivity (PPC) of the Te channel revealed that the relaxation speed of the transient photocurrent depended on the gate bias. Utilizing the PPC property, the Te device served as an excellent photonic synapse under light pulse stimulus, exhibiting multiple synaptic characteristics such as excitatory postsynaptic current and paired-pulse facilitation, as well as highly linear potentiation-depression characteristics; a simulation-based study further confirmed the effectiveness of the device. Most importantly, by exploiting the nonlinear and fading memory characteristics of the Te photonic synapse, we demonstrate two advanced examples of PRC. In classifying handwritten digits, our system carried out successful digit recognition without binarization or another simplification process with reduced computational cost compared to conventional systems. To solve second-order nonlinear equations, we introduce the strategy of utilizing historical nodes. The combination of historical nodes and the gate-tunable responses of the photonic synapses, which provide an enriched reservoir state, yielded excellent prediction accuracy. Overall, this work will offer an understanding of Te-based optoelectronic devices and their synergetic integration with neuromorphic devices and PRC.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared error of 1.06 × 10⁻3 during prediction).

In-Situ Fabricated Transparent Flexible Nanowire Device with Wavelength-Regulated Dual-Function of Photodetector and Photonic Synapse

Integrating the dual functionalities of a photodetector and photonic synapse into a single device is challenging due to their conflicting requirements for photocurrent decay rates. This study addresses this issue by seamlessly depositing transparent indium tin oxide (ITO) electrodes onto self-oriented copper hexadecafluoro-phthalocyanine (F16CuPc) nanowires growing horizontally along hot-stamped periodic nanogrooves on a transparent flexible polyimide plastic film. This in-situ-fabricated device achieves bending-stable dual functionalities through wavelength regulation while maintaining high transparency and flexibility. Upon exposure to 450-850 nm light, the device exhibits a rapid and sensitive photoresponse with excellent bending stability, making it ideal for optical sensing in both visible and near-infrared spectra. More importantly, the device exhibits a bending-stable excitation postsynaptic current when exposed to light spikes below 405 nm. This enables the successful emulation of various biological synaptic functionalities, including paired-pulse facilitation, spike-number-dependent plasticity, spike-duration-dependent plasticity, spike-rating-dependent plasticity, configurable plasticity between short-term plasticity and long-term plasticity, and memory learning capabilities. Utilizing this device in an artificial neural network achieves a recognition rate of 95% after 57 training epochs. Its ability to switch between photodetection and synaptic modes by adjusting the light wavelength marks a significant advancement in the field of multifunctional flexible electronics based on nanowire arrays.

Analog reservoir computing via ferroelectric mixed phase boundary transistors

Analog reservoir computing (ARC) systems have attracted attention owing to their efficiency in processing temporal information. However, the distinct functionalities of the system components pose challenges for hardware implementation. Herein, we report a fully integrated ARC system that leverages material versatility of the ferroelectric-to-mixed phase boundary (MPB) hafnium zirconium oxides integrated onto indium-gallium-zinc oxide thin-film transistors (TFTs). MPB-based TFTs (MPBTFTs) with nonlinear short-term memory characteristics are utilized for physical reservoirs and artificial neuron, while nonvolatile ferroelectric TFTs mimic synaptic behavior for readout networks. Furthermore, double-gate configuration of MPBTFTs enhances reservoir state differentiation and state expansion for physical reservoir and processes both excitatory and inhibitory pulses for neuronal functionality with minimal hardware burden. The seamless integration of ARC components on a single wafer executes complex real-world time-series predictions with a low normalized root mean squared error of 0.28. The material-device co-optimization proposed in this study paves the way for the development of area- and energy-efficient ARC systems.

Tunable synaptic behaviors of solution-processed InGaO films for artificial visual systems

Due to their persistent photoconductivity, amorphous metal oxide thin films are promising for construction of artificial visual systems. In this work, large-scale, uniformly distributed amorphous InGaO thin films with an adjustable In/Ga ratio and thickness are prepared successfully by a low-cost environmentally friendly and easy-to-handle solution process for constructing artificial visual systems. With the increase of the In/Ga ratio and film thickness, the number of oxygen vacancies increases, along with the increase of post-synaptic current triggered by illumination, benefiting the transition of short-term plasticity to long-term plasticity. With an optimal In/Ga ratio and film thickness, the conductance response difference at a decay of 0 s between the 1st and the 10th views of a 5 × 5 array InGaO thin film transistor is up to 2.88 μA, along with an increase in the Idecay 30s/Idecay 0s ratio from 45.24% to 53.24%, resulting in a high image clarity and non-volatile artificial visual memory. Furthermore, a three-layer artificial vision network is constructed to evaluate the image recognition capability, exhibiting an accuracy of up to 91.32%. All results promise low-cost and easy-to-handle amorphous InGaO thin films for future visual information processing and image recognition.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

An Active-Matrix Synaptic Phototransistor Array for In-Sensor Spectral Processing

The human retina perceives and preprocesses the spectral information of incident light, enabling fast image recognition and efficient chromatic adaptation. In comparison, it is reluctant to implement parallel spectral preprocessing and temporal information fusion in current complementary metal-oxide-semiconductor (CMOS) image sensors, requiring intricate circuitry, frequent data transmission, and color filters. Herein, an active-matrix synaptic phototransistor array (AMSPA) is developed based on organic/inorganic semiconductor heterostructures. The AMSPA provides wavelength-dependent, bidirectional photoresponses, enabling dynamic imaging and in-sensor spectral preprocessing functions. Specifically, near-infrared light induces inhibitory photoresponse while UV light results in exhibitory photoresponse. With rational structural design of the organic/inorganic hybrid heterostructures, the current dynamic range of phototransistor is improved to over 90 dB. Finally, a 32 × 64 AMSPA (128 pixels per inch) is demonstrated with one-switch-transistor and one-synaptic phototransistor (1-T-1-PT) structure, achieving spatial chromatic enhancement and temporal trajectory imaging. These results reveal the feasibility of AMSPA for constructing artificial vision systems.

Birdlike broadband neuromorphic visual sensor arrays for fusion imaging

Wearable visual bionic devices, fueled by advancements in artificial intelligence, are making remarkable progress. However, traditional silicon vision chips often grapple with high energy losses and challenges in emulating complex biological behaviors. In this study, we constructed a van der Waals P3HT/GaAs nanowires P-N junction by carefully directing the arrangement of organic molecules. Combined with a Schottky junction, this facilitated multi-faceted birdlike visual enhancement, including broadband non-volatile storage, low-light perception, and a near-zero power consumption operating mode in both individual devices and 5 × 5 arrays on arbitrary substrates. Specifically, we realized over 5 bits of in-memory sensing and computing with both negative and positive photoconductivity. When paired with two imaging modes (visible and UV), our reservoir computing system demonstrated up to 94% accuracy for color recognition. It achieved motion and UV grayscale information extraction (displayed with sunscreen), leading to fusion visual imaging. This work provides a promising co-design of material and device for a broadband and highly biomimetic optoelectronic neuromorphic system.

An overview of critical applications of resistive random access memory

The rapid advancement of new technologies has resulted in a surge of data, while conventional computers are nearing their computational limits. The prevalent von Neumann architecture, where processing and storage units operate independently, faces challenges such as data migration through buses, leading to decreased computing speed and increased energy loss. Ongoing research aims to enhance computing capabilities through the development of innovative chips and the adoption of new system architectures. One noteworthy advancement is Resistive Random Access Memory (RRAM), an emerging memory technology. RRAM can alter its resistance through electrical signals at both ends, retaining its state even after power-down. This technology holds promise in various areas, including logic computing, neural networks, brain-like computing, and integrated technologies combining sensing, storage, and computing. These cutting-edge technologies offer the potential to overcome the performance limitations of traditional architectures, significantly boosting computing power. This discussion explores the physical mechanisms, device structure, performance characteristics, and applications of RRAM devices. Additionally, we delve into the potential future adoption of these technologies at an industrial scale, along with prospects and upcoming research directions.

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Machine Learning Assisted Self-Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature-driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H2O/glycerol binary solvent with [Fe(CN)6]3-/4- as a redox couple. Using a concave-arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self-existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self-powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human-machine interaction toward future Internet of everything.

Controlled growth of asymmetric chiral TeOx for broad-spectrum, high-responsivity and polarization-sensitive photodetection

Low-dimensional nanostructures, especially one-dimensional materials, exhibit remarkable anisotropic characteristics due to their low symmetry, making them promising candidates for polarization-sensitive photodetection. Here, we present a chemical vapor deposition synthesis method for tellurium suboxide (TeOx), confirming the practicality of photodetectors constructed from TeOx nanowires (NWs) in high-responsivity, broadband, and polarization-sensitive detection. By precisely controlling the thermodynamics and kinetics of TeOx NWs growth, we achieve large-scale growth of TeOx NWs with highly controllable dimensions and propose a method to induce intrinsic built-in strain in TeOx NWs. Photodetectors based on quasi-one-dimensional TeOx NWs with ohmic contact demonstrate broadband spectral response (638-1550 nm), high responsivity (13 700 mA·W-1), and superior air stability. Particularly, owing to the inherent structural anisotropy of the photodetectors, they exhibit polarization-sensitive photodetection, with anisotropy ratios of 1.70 and 1.71 at 638 and 808 nm, respectively.

A nonconjugated radical polymer enables bimodal memory and in-sensor computing operation

This study reports intrinsic multimodal memristivity of a nonconjugated radical polymer with ambient stability. Organic memristive devices represent powerful candidates for biorealistic data storage and processing. However, there exists a substantial knowledge gap in realizing the synthetic biorealistic systems capable of effectively emulating the cooperative and multimodal activation processes in biological systems. In addition, conventional organic memristive materials are centered on conjugated small and macromolecules, making them synthetically challenging or difficult to process. In this work, we first describe the intrinsic resistive switching of the radical polymer that resulted in an exceptional state retention of >105 s and on/off ratio of >106. Next, we demonstrate its bimodal cooperative switching, in response to the proton accumulation as a biological input. Last, we expand our system toward an advanced in-sensor computing system. Our research demonstrates a nonconjugated radical polymer with intrinsic memristivity, which is directly applicable to future electronics including data storage, neuromorphics, and in-sensor computing.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography

The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengths and mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

Pre-sensor computing with compact multilayer optical neural network

Moving computation units closer to sensors is becoming a promising approach to addressing bottlenecks in computing speed, power consumption, and data storage. Pre-sensor computing with optical neural networks (ONNs) allows extensive processing. However, the lack of nonlinear activation and dependence on laser input limits the computational capacity, practicality, and scalability. A compact and passive multilayer ONN (MONN) is proposed, which has two convolution layers and an inserted nonlinear layer, performing pre-sensor computations with designed passive masks and a quantum dot film for incoherent light. MONN has an optical length as short as 5 millimeters, two orders of magnitude smaller than state-of-the-art lens-based ONNs. MONN outperforms linear single-layer ONN across various vision tasks, off-loading up to 95% of computationally expensive operations into optics from electronics. Motivated by MONN, a paradigm is emerging for mobile vision, fulfilling the demands for practicality, miniaturization, and low power consumption.

Dynamic memristor for physical reservoir computing

Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.

Integrating ultraviolet sensing and memory functions in gallium nitride-based optoelectronic devices

Optoelectronic devices present a promising avenue for emulating the human visual system. However, existing devices struggle to maintain optical image information after removing external stimuli, preventing the integration of image perception and memory. The development of optoelectronic memory devices offers a feasible solution to bridge this gap. Simultaneously, the artificial vision for perceiving and storing ultraviolet (UV) images is particularly important because UV light carries information imperceptible to the naked eye. This study introduces a multi-level UV optoelectronic memory based on gallium nitride (GaN), seamlessly integrating UV sensing and memory functions within a single device. The embedded SiO2 side-gates around source and drain regions effectively extend the lifetime of photo-generated carriers, enabling dual-mode storage of UV signals in terms of threshold voltage and ON-state current. The optoelectronic memory demonstrates excellent robustness with the retention time exceeding 4 × 104 s and programming/erasing cycles surpassing 1 × 105. Adjusting the gate voltage achieves five distinct storage states, each characterized by excellent retention, and efficiently modulates erasure times for rapid erasure. Furthermore, the integration of the GaN optoelectronic memory array successfully captures and stably stores specific UV images for over 7 days. The study marks a significant stride in optoelectronic memories, showcasing their potential in applications requiring prolonged retention.

Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems

Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.

Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems

The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.

Pyroelectric Photoconductive Diode for Highly Sensitive and Fast DUV Detection

Detecting high-energy photons from the deep ultraviolet (DUV) to X-rays is vital in security, medicine, industry, and science. Wide bandgap (WBG) semiconductors exhibit great potential for detecting high-energy photons. However, the implementation of highly sensitive and high-speed detectors based on WBG semiconductors has been a huge challenge due to the inevitable deep level traps and the lack of appropriate device structure engineering. Here, a sensitive and fast pyroelectric photoconductive diode (PPD), which couples the interface pyroelectric effect with the photoconductive effect based on tailored polycrystal Ga-rich GaOx (PGR-GaOx) Schottky photodiode, is first proposed. The PPD device exhibits ultrahigh detection performance for DUV and X-ray light. The responsivity for DUV light and sensitivity for X-ray are up to 104 A W-1 and 105 µC Gyair -1 cm-2, respectively. Especially, the interface pyroelectric effect induced by polar symmetry in the depletion region of the PGR-GaOx can significantly improve the response speed of the device by 105 times. Furthermore, the potential of the device is demonstrated for imaging enhancement systems with low power consumption and high sensitivity. This work fully excavates the potential of the pyroelectric effect for detectors and provides a novel design strategy to achieve sensitive and high-speed detectors.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis

Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.