Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Near-Sensor Neuromorphic Computing System Based on a Thermopile Infrared Detector and a Memristor for Encrypted Visual Information Transmission

Near-sensor neuromorphic computing systems that utilize photodetectors and memristors exhibit significant promise in the domains of visual information processing, transmission, and noise reduction recognition. In comparison to conventional photodetectors operating within the visible-light spectrum, thermopile infrared detectors offer distinct advantages in terms of concealment and security. This study proposes an integrated near-sensor computing system that combines a thermoelectric infrared detector with a memristor, which demonstrates a broad detection range (100-310 °C), rapid response time for sensing infrared signals, and excellent neuromorphic computing characteristics for information processing. Besides high-accuracy recognition of handwritten digits, near-infrared visual information recognition and voice recognition for double information encryption were demonstrated in the system. This neuromorphic computing system holds considerable potential for applications in the propagation, encryption, and recognition of security information within the infrared spectrum.

Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications

In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.

Strategic Development of Memristors for Neuromorphic Systems: Low-Power and Reconfigurable Operation

The ongoing global energy crisis has heightened the demand for low-power electronic devices, driving interest in neuromorphic computing inspired by the parallel processing of human brains and energy efficiency. Reconfigurable memristors, which integrate both volatile and non-volatile behaviors within a single unit, offer a powerful solution for in-memory computing, addressing the von Neumann bottleneck that limits conventional computing architectures. These versatile devices combine the high density, low power consumption, and adaptability of memristors, positioning them as superior alternatives to traditional complementary metal-oxide-semiconductor (CMOS) technology for emulating brain-like functions. Despite their potential, studies on reconfigurable memristors remain sparse and are often limited to specific materials such as Mott insulators without fully addressing their unique reconfigurability. This review specifically focuses on reconfigurable memristors, examining their dual-mode operation, diverse physical mechanisms, structural designs, material properties, switching behaviors, and neuromorphic applications. It highlights the recent advancements in low-power-consumption solutions within memristor-based neural networks and critically evaluates the challenges in deploying reconfigurable memristors as standalone devices or within artificial neural systems. The review provides in-depth technical insights and quantitative benchmarks to guide the future development and implementation of reconfigurable memristors in low-power neuromorphic computing.

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.

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.

Universal Magnetic-Conductive Interfaces Enabling Reversible Interconnections in Fiber Electronics

Fiber electronics have gained significant attention for their seamless integration into textiles, offering enhanced flexibility, breathability, and wearability. However, conventional circuit assembly methods for fiber devices often rely on rigid and irreversible connections, such as silver conductive adhesives, which impede essential modifications in electronic textiles like energy module upgrades, sensor replacements, and damage repair. Here, a universal magnetic-conductive (MC) interface that facilitates robust, reversible interconnections for fiber electronics is presented. Fabricated by sequentially depositing a magnetic interlayer and a conductive sheath on electrode ends using a coating die, this interface is compatible with diverse fiber devices. Upon close proximity, the MC interfaces magnetically attract and establish a stable electrical pathway spontaneously (<40 ms), delivering conductivity comparable to cured silver paste. Remarkably, the connection retains its performance over 10 000 connect-disconnect cycles, mechanical swinging cycles, and operations under diverse environmental conditions. The versatility of the MC interface is further demonstrated by creating a detachable fabric power bank to power wearable devices and integrating a removable signal-processing textile for on-demand electrocardiogram monitoring in rats. Overall, this innovation establishes a universal, scalable platform for the assembly of fiber electronics, paving the way for next-generation customizable wearable devices.

Nanofluidic Memristive Transition and Synaptic Emulation in Atomically Thin Pores

Ionic transport across nanochannels is the basis of communications in living organisms, enlightening neuromorphic nanofluidic iontronics. Comparing to the angstrom-scale long biological ionic pathways, it remains a great challenge to achieve nanofluidic memristors at such thinnest limit due to the ambiguous electrical model and interaction process. Here, we report atomically thin memristive nanopores in two-dimensional materials by designing optimized ionic conductance to decouple the memristive, ohmic, and capacitive effects. By conducting different charged iontronics, we realize the reconfigurable memristive transition between nonvolatile-bipolar and volatile-unipolar characteristics, which arises from distinct transport processes governed by energy barriers. Notably, we emulate synaptic functions with ultralow energy consumption of ∼0.546 pJ per spike and reproduce biological learning behaviors. The memristive nanopores are similar to the biosystems in angstrom structure, rich iontronic responses, and millisecond-level operating pulse width, matching the biological potential width. This work provides a new paradigm for boosting brain-inspired nanofluidic devices.

Intrinsically Stretchable Resistive Memory Devices Utilizing Wavy Structured Strategy Integrated with Metal-Organic Framework Glasses

Flexible resistive random-access memory (RRAM) holds significant promise for data storage applications in the realms of smart healthcare and wearable devices. However, most research has focused primarily on the development of stretchable electrodes, frequently neglecting the mechanical compatibility between the functional layer and the electrode. Consequently, the advancement of intrinsically stretchable memristors presents a substantial challenge. Herein, a glassy metal-organic framework (MOF) film with a wrinkle structure is integrated with a pre-stretched electrode to fabricate intrinsically stretchable memristors. These devices demonstrate an impressive switching ratio of up to 105, a bending radius limit of 10 mm, and a strain limit of 20%, all while maintaining stable switching characteristics. Furthermore, conductive atomic force microscope (C-AFM) and focused ion beam (FIB) techniques reveal that the resistive switching effect is primarily governed by the silver conductive filament mechanism. This work successfully developed an intrinsically stretchable memristor, paving the way for the application of MOFs as functional layers in flexible electronics. It is expected to inspire further application of MOFs in the design of high-performance, flexible electronic technologies.

Mechano-Driven Neuromimetic Logic Gates Established by Geometrically Asymmetric Hydrogel Iontronics

The human brain's neural network demonstrates exceptional efficiency in information processing and recognition, driving advancements in neuromimetic devices that emulate neuronal functions such as signal integration and parallel transmission. A key challenge remains in replicating these functions while minimizing energy consumption. Here, inspired by neuronal signal integration and axonal bidirectional transmission, mechano-driven hydrogel logic gates leveraging the piezoionic effect is presented, offering a novel bionic approach with significantly reduced power consumption. By exerting external force on the thick and thin sides of the geometrically asymmetric hydrogel, spike signals of differing amplitudes and opposite polarities can be generated, corresponding to '1' and '0', respectively. The differential mobility of anions and cations plays a crucial role in the piezoionic effect. This geometric asymmetry amplifies ion convection, improving force-to-electricity conversion efficiency, while the inclusion of salts with varying ion size can further enhance this disparity, even reversing the signal direction. Arranging asymmetric hydrogel iontronics in series-parallel configurations enables the emulation of complex neuronal logic operations, facilitating ionic spike signal addition and subtraction. This hydrogel-based logic control has been directly applied in human-machine interaction to control robot arms and offers significant potential for the advancement of artificial intelligence, robotics, and wearable technologies.

In-Sensor Computing with Visual-Tactile Perception Enabled by Mechano-Optical Artificial Synapse

In-sensor computing paradigm holds the promise of realizing rapid and low-power signal processing. Constructing crossmodal in-sensor computing systems to emulate human sensory and recognition capabilities has been a persistent pursuit for developing humanoid robotics. Here, an artificial mechano-optical synapse is reported to implement in-sensor dynamic computing with visual-tactile perception. By employing mechanoluminescence (ML) material, direct conversion of the mechanical signals into light emission is achieved and the light is transported to an adjacent photostimulated luminescence (PSL) layer without pre- and post-irradiation. The PSL layer acts as a photon reservoir as well as a processing unit for achieving in-memory computing. The approach based on ML coupled with PSL material is different from traditional circuit-constrained methods, enabling remote operation and easy accessibility. Individual and synergistic plasticity are elaborately investigated under force and light pulses, including paired-pulse facilitation, learning behavior, and short-term and long-term memory. A multisensory neural network is built for processing the obtained handwritten patterns with a tablet consisting of the device, achieving a recognition accuracy of up to 92.5%. Moreover, material identification has been explored based on visual-tactile sensing, with an accuracy rate of 98.6%. This work provides a promising strategy to construct in-sensor computing systems with crossmodal integration and recognition.

Neuromorphic devices for electronic skin applications

Neuromorphic devices represent an important advancement in technology, drawing inspiration from the intricate and efficient mechanisms of the human brain. This review paper elucidates the diverse landscape of neuromorphic electronic skin (e-skin) technologies while highlighting their numerous applications. Here, neuromorphic devices for e-skin are classified as two types of direct neuromorphic e-skins combining both neuromorphic devices and sensors, and indirect e-skins separating neuromorphic devices and sensors. In direct neuromorphic e-skins, there are developing devices like memristor-based neuromorphic devices with sensors and transistor-based neuromorphic devices with sensors. On the other hand, indirect types are demonstrated as separated neuromorphic and sensor parts systems through the various interfacing structures. It also describes recent neuromorphic developments in artificial neural networks (ANNs), deep neural networks (DNNs), and convolutional neural networks (CNNs), for the real-time interpretation of sensory data. Moreover, it introduces multimodal sensory feedback, soft and flexible e-skins, and more intuitive human-machine interfaces. This review examines various applications, including smart textiles for the development of next-generation wearable bioelectronics, brain-sensing interfaces that enhance tactile perception, and the integration of human-machine interfaces aimed at replicating the biological sensorimotor loop, which can improve health monitoring and biomedical applications. Additionally, the review also highlights the potential of neuromorphic e-skin in human-robot interaction, particularly in the context of continuous prosthetic control and robotics. Through this analysis, the paper provides insights into current advancements, identifies key challenges, and suggests future research directions for optimizing neuromorphic e-skin devices and expanding their practical implementation.

Reconfigurable Al2O3-Based Memristor for All-in-One Artificial Synapse and Nociceptor Neurons

Multifunctional bionic devices have widespread applications in neuromorphic computing, intelligent sensors, and robotics. The inherent properties of memristors make them suitable for these emerging applications, but different applications require either volatile or nonvolatile operations in a unique device. In this work, we have developed a novel reconfigurable Ag/Al2O3/ITO memristor, which achieves adjustable switching behavior between volatile switching and nonvolatile switching by modulating the compliance current. A proposed mechanism controls the state of the conductive filaments in the device by adjusting compliance current, elucidating the adjustable switching process between volatile and nonvolatile states. Additionally, the synaptic functionality and nociceptor characteristics, including threshold, relaxation, inadaptation, and sensitization, have been successfully simulated. This integration of artificial synaptic and nociceptor functions into a single device is achieved, with the single-pulse power consumption of the nociceptor reaching as low as 0.912 nJ when the threshold is reached. These results provide insights into the construction of multifunctional bionic devices and demonstrate significant potential for future neuromorphic network applications.

Biomaterials for reliable wearable health monitoring: Applications in skin and eye integration

Recent advancements in biomaterials have significantly impacted wearable health monitoring, creating opportunities for personalized and non-invasive health assessments. These developments address the growing demand for customized healthcare solutions. Durability is a critical factor for biomaterials in wearable applications, as they must withstand diverse wearing conditions effectively. Therefore, there is a heightened focus on developing biomaterials that maintain robust and stable functionalities, essential for advancing wearable sensing technologies. This review examines the biomaterials used in wearable sensors, specifically those interfaced with human skin and eyes, highlighting essential strategies for achieving long-lasting and stable performance. We specifically discuss three main categories of biomaterials-hydrogels, fibers, and hybrid materials-each offering distinct properties ideal for use in durable wearable health monitoring systems. Moreover, we delve into the latest advancements in biomaterial-based sensors, which hold the potential to facilitate early disease detection, preventative interventions, and tailored healthcare approaches. We also address ongoing challenges and suggest future directions for research on material-based wearable sensors to encourage continuous innovation in this dynamic field.

Flexible Synaptic Memristors With Controlled Rigidity in Zirconium-Oxo Clusters for High-Precision Neuromorphic Computing

Flexible memristors are promising candidates for multifunctional neuromorphic computing applications, overcoming the limitations of conventional computing devices. However, unpredictable switching behavior and poor mechanical stability in conventional memristors present significant challenges to achieving device reliability. Here, a reliable and flexible memristor using zirconium-oxo cluster (Zr6O4OH4(OMc)12) as the resistive switching layer is demonstrated. The optimization of the structural rigidity of the hybrid oxo-cluster network by thermal polymerization allows the precise formation of dispersed conductive cluster networks, enhancing the repeatability of the resistive switching with mechanical flexibility. The optimized memristor exhibits endurance of ∼104 cycles and stable memory retention performance up to 104 s, maintaining a high ION/IOFF ratio of 104 under a bending radius of 2.5 mm. Moreover, the device achieves a pattern recognition accuracy of 97.44%, enabled by highly symmetric analog switching with multilevel conductance states. These results highlight that hybrid metal-oxo clusters can provide novel material design principles for flexible and reliable neuromorphic applications, contributing to the development of artificial neural networks.

DC field-biased multibit/analog artificial synapse featuring an additional degree of freedom for performance tuning

Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO2/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term. The unbiased device is of short-term plasticity, positive biasing increases the remanence of the recorded events and the device gains long-term plasticity at a specific biasing level determined from the device geometry. The adjustability of the biasing field provides an additional degree of freedom allowing performance tuning; the paired-pulse facilitation index of the device is tuned by the biasing level adjustment providing further functional versatility. An appropriately biased segment provides more than 10 synaptic weight levels linearly depending on the number and duration of the stimulating spikes. The relationship with spike magnitude is exponential. The experimentally determined nonlinearity coefficient of the biased device for 50 potentiating spikes is comparable to the best published data. The spike-timing-dependent plasticity determined experimentally for the biased device in its long-term plasticity mode fits the mathematical relationship developed for biological synapses. Fabricated on a titanium metal foil, the produced memristors are sturdy and flexible making them suitable for wearable and implantable intelligent electronics. Our findings are anticipated to raise the potential of forming artificial synapses out of polycrystalline metal oxide thin films.

Two-Dimensional Materials for Brain-Inspired Computing Hardware

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.

IGZO-Based Electronic Device Application: Advancements in Gas Sensor, Logic Circuit, Biosensor, Neuromorphic Device, and Photodetector Technologies

Metal oxide semiconductors, such as indium gallium zinc oxide (IGZO), have attracted significant attention from researchers in the fields of liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) for decades. This interest is driven by their high electron mobility of over ~10 cm2/V·s and excellent transmittance of more than ~80%. Amorphous IGZO (a-IGZO) offers additional advantages, including compatibility with various processes and flexibility making it suitable for applications in flexible and wearable devices. Furthermore, IGZO-based thin-film transistors (TFTs) exhibit high uniformity and high-speed switching behavior, resulting in low power consumption due to their low leakage current. These advantages position IGZO not only as a key material in display technologies but also as a candidate for various next-generation electronic devices. This review paper provides a comprehensive overview of IGZO-based electronics, including applications in gas sensors, biosensors, and photosensors. Additionally, it emphasizes the potential of IGZO for implementing logic gates. Finally, the paper discusses IGZO-based neuromorphic devices and their promise in overcoming the limitations of the conventional von Neumann computing architecture.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

Refreshable memristor via dynamic allocation of ferro-ionic phase for neural reuse

Neural reuse can drive organisms to generalize knowledge across various tasks during learning. However, existing devices mostly focus on architectures rather than network functions, lacking the mimic capabilities of neural reuse. Here, we demonstrate a rational device designed based on ferroionic CuInP2S6, to accomplish the neural reuse function, enabled by dynamic allocation of the ferro-ionic phase. It allows for dynamic refresh and collaborative work between volatile and non-volatile modes to support the entire neural reuse process. Notably, ferroelectric polarization can remain consistent even after undergoing the refresh process, providing a foundation for the shared functionality across multiple tasks. By implementing neural reuse, the classification accuracy of neuromorphic hardware can improve by 17%, while the consumption is reduced by 40%; in multi-task scenarios, its training speed is accelerated by 2200%, while its generalization ability is enhanced by 21%. Our results are promising towards building refreshable hardware platforms based on ferroelectric-ionic combination capable of accommodating more efficient algorithms and architectures.

High-Speed and Low-Energy Resistive Switching with Two-Dimensional Cobalt Phosphorus Trisulfide for Efficient Neuromorphic Computing

Two-dimensional (2D) materials hold significant potential for the development of neuromorphic computing architectures owing to their exceptional electrical tunability, mechanical flexibility, and compatibility with heterointegration. However, the practical implementation of 2D memristors in neuromorphic computing is often hindered by the challenges of simultaneously achieving low latency and low energy consumption. Here, we demonstrate memristors based on 2D cobalt phosphorus trisulfide (CoPS3), which achieve impressive performance metrics including high switching speed (20 ns), low switching energy (1.15 pJ), high switching ratio (>400), and low switching voltages (1.05 V for set and -0.89 V for reset). The creation of sulfur vacancies in CoPS3 through an electroforming process facilitates the formation of conductive filaments, leading to uniform fast switching with minimal energy requirements. The CoPS3 memristors also show linear conductance modulation and long-term memory retention, enabling high-accuracy modeling of artificial neural networks for handwritten digit recognition and convolutional neural networks for image processing. Furthermore, robust memristive switching is achieved in solution-processed large-scale CoPS3 films, underscoring their potential for wafer-scale, low-temperature integration. The combination of rapid switching, low energy consumption, extended memory retention, high switching ratio, linear conductance update, and scalability manifests the potential of 2D CoPS3 materials for energy-efficient neuromorphic computing circuits.

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.

Effect of Lanthanum-Aluminum Co-Doping on Structure of Hafnium Oxide Ferroelectric Crystals

Hafnium oxide (HfO2)-based devices have been extensively evaluated for high-speed and low-power memory applications. Here, the influence of aluminum (Al) and lanthanum (La) co-doping HfO2 thin films on the ferroelectric characteristics of hafnium-based devices is investigated. Among devices with different La/Al ratios, the Al and La co-doped hafnium oxide (HfAlAO) device with 4.2% Al and 2.17% La exhibited the excellent remanent polarization and thermostability. Meanwhile, first principal analyses verified that hafnium-based thin films with 4.2% Al and 2.17% La promoted the formation of the o-phase against the paraelectric phase, providing theoretical support for supporting experimental results. Furthermore, a vertical ferroelectric HfO2 memory based on 3D macaroni architecture is reported. The devices show excellent ferroelectric characteristics of 22 µC cm-2 under 4.5 MV cm-1 and minimal coercive field of ≈1.6 V. In addition, the devices exhibit great memory performance, including the response speed of device can achieve 20 ns and endurance characteristic can achieve 1010 cycles.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Outstanding Stability and Resistive Switching Performance through Octa-Amino-Polyhedral Oligomeric Silsesquioxane Modification in Flexible Perovskite Resistive Random-Access Memories

Resistive random access memory (RRAM) has emerged as a promising candidate for next-generation storage technologies due to its simple structure, high running speed, excellent durability, high integration density, and low power consumption. This paper focuses on the application of organic-inorganic hybrid perovskite (OIHP) materials in RRAM by introducing an innovative three-dimensional POPA modification strategy, which is realized by binding octa-amine-polyhedral oligomeric silsesquioxanes (8NH2-POSS) onto the side chains of poly(acrylic acid) (PAA), thereby enhancing the material's resilience under elevated temperatures and humidity conditions. POPA cross-links with perovskite grains at crystalline boundaries through multiple -NH3+ and -C═O chemical anchoring sites on its branch chain, enhancing the grain adhesion, optimizing the film quality, and improving the cage structure distribution at the perovskite grain boundaries. The experimental results demonstrate that the POPA-modified OIHP RRAM exhibits an excellent resistance switching performance, with an optimal ON/OFF ratio of 5.0 × 105 and a data retention time of 104 s. After 150 days of environmental exposure, the ON/OFF ratio remains at 1.0 × 105, indicating good stability. Furthermore, the POPA modification endows the perovskite film with considerable flexibility, maintaining stable resistance switching performance under various bending radii. This study provides a vital reference for flexible, high-performance, and long-lifespan perovskite memory devices.

Configurable Synaptic and Stochastic Neuronal Functions in ZnTe-Based Memristor for an RBM Neural Network

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (µA) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.

Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing

Recently, the rising demand for data-based applications has driven the convergence of image sensing, memory, and computing unit interfaces. While specialized electronic hardware has spurred advancements in the in-memory and in-sensor computing, integrating the entire signal-processing chain into a single device still faces significant challenges. Here, a reconfigurable all-optical controlled memristor with the selector-free feature is demonstrated. The conductance of the device can be controlled within the pure light domain, which enables it to integrate sensing, memory, and computing together. The integrate-and-fire behavior is also realized through electrical stimuli. Furthermore, the device exhibits an excellent rectifying ratio and nonlinearity to overcome the sneak current. Finally, an in-memory sensing and computing architecture is realized through reservoir computing based on neuron and synaptic functions mimicked by the proposed device. Such an all-in-one paradigm facilitates the computing architecture with low energy consumption, low latency, and reduced hardware complexity.

Molecular switching by proton-coupled electron transport drives giant negative differential resistance

To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) × 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks.

Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing

Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.

Electronically Reconfigurable Memristive Neuron Capable of Operating in Both Excitation and Inhibition Modes

Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

Fibres-threads of intelligence-enable a new generation of wearable systems

Fabrics represent a unique platform for seamlessly integrating electronics into everyday experiences. The advancements in functionalizing fabrics at both the single fibre level and within constructed fabrics have fundamentally altered their utility. The revolution in materials, structures, and functionality at the fibre level enables intimate and imperceptible integration, rapidly transforming fibres and fabrics into next-generation wearable devices and systems. In this review, we explore recent scientific and technological breakthroughs in smart fibre-enabled fabrics. We examine common challenges and bottlenecks in fibre materials, physics, chemistry, fabrication strategies, and applications that shape the future of wearable electronics. We propose a closed-loop smart fibre-enabled fabric ecosystem encompassing proactive sensing, interactive communication, data storage and processing, real-time feedback, and energy storage and harvesting, intended to tackle significant challenges in wearable technology. Finally, we envision computing fabrics as sophisticated wearable platforms with system-level attributes for data management, machine learning, artificial intelligence, and closed-loop intelligent networks.

Fabrication, sustainability, and key performance indicators of bioelectronics via fiber building blocks

Bioelectronics provide efficient information exchange between living systems and man-made devices, acting as a vital bridge in merging the domains of biology and technology. Using functional fibers as building blocks, bioelectronics could be hierarchically assembled with vast design possibilities across different scales, enhancing their application-specific biointegration, ergonomics, and sustainability. In this work, the authors review recent developments in bioelectronic fiber elements by reflecting on their fabrication approaches and key performance indicators, including the life cycle sustainability, environmental electromechanical performance, and functional adaptabilities. By delving into the challenges associated with physical deployment and exploring innovative design strategies for adaptability, we propose avenues for future development of bioelectronics via fiber building blocks, boosting the potential of "Fiber of Things" for market-ready bioelectronic products with minimized environmental impact.

Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

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.

Reconfigurable Neuromorphic Computing with 2D Material Heterostructures for Versatile Neural Information Processing

Reconfigurable neuromorphic computing holds promise for advancing energy-efficient neural network implementation and functional versatility. Previous work has focused on emulating specific neural functions rather than an integrated approach. We propose an all two-dimensional (2D) material-based heterostructure capable of performing multiple neuromorphic operations by reconfiguring output terminals in response to stimuli. Specifically, our device can synergistically emulate the key neural elements of the synapse, neuron, and dendrite, which play important and interrelated roles in information processing. Dendrites, the branches that receive and transmit presynaptic action potentials, possess the ability to nonlinearly integrate and filter incoming signals. The proposed heterostructure allows reconfiguration between different operation modes, demonstrating its potential for diverse computing tasks. As a proof of concept, we show that the device can perform basic Boolean logic functions. This highlights its applicability to complex neural-network-based information processing problems. Our integrated neuromorphic approach may advance the development of versatile, low-power neuromorphic hardware.

Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory

Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.

High-Performance LiNbO3 Domain Wall Memory Devices with Enhanced Selectivity via Optimized Metal-Semiconductor Contact

Lithium niobate (LiNbO3) single-crystal nanodevices featuring elevated readout domain wall currents exhibit significant potential for integrated circuits in memory computing applications. Nevertheless, challenges stem from suboptimal electrode-LiNbO3 single crystal contact characteristics, which impact the stability of high currents within these devices. In this work, we concentrate on augmenting the domain wall current by refining the fabrication processes of domain wall random access memory (DWRAM). Each LiNbO3 domain wall nanodevice was fabricated using a self-aligned process. Device performance was significantly enhanced by introducing a 10 nm interlayer between the LiNbO3 and Cu electrodes. A comparative analysis of electrical properties was conducted on devices with interlayers made of chromium (Cr) and titanium (Ti), as well as devices without interlayers. After the introduction of the Ti interlayer, the device's coercive voltage demonstrated an 82% reduction, while the current density showed a remarkable 94-fold increase. A 100 nm sized device with the Ti interlayer underwent positive down-negative up pulse testing, demonstrating a writing time of 82 ns at 8 V and an erasing time of 12 μs at -9 V. These operating speeds are significantly faster than those of devices without interlayers. Moreover, the enhanced devices exhibited symmetrical domain switching hysteresis loops with retention times exceeding 106 s. Notably, the coercive voltage (Vc) dispersion remained narrow after more than 1000 switching cycles. At an elevated temperature of 400 K, the device's on/off ratio was maintained at 105. The device's embedded selector demonstrated an ultrahigh selectivity (>106) across various reading voltages. These results underscore the viability of high-density nanoscale integration of ferroelectric domain wall memory.

Modulation of polyaniline memristive device switching voltage by nucleotide-free analogue of vitamin B12

Memristive devices offer essential properties to become a part of the next-generation computing systems based on neuromorphic principles. Organic memristive devices exhibit a unique set of properties which makes them an indispensable choice for specific applications, such as interfacing with biological systems. While the switching rate of organic devices can be easily adjusted over a wide range through various methods, controlling the switching potential is often more challenging, as this parameter is intricately tied to the materials used. Given the limited options in the selection conductive polymers and the complexity of polymer chemical engineering, the most straightforward and accessible approach to modulate switching potentials is by introducing specific molecules into the electrolyte solution. In our study, we show polyaniline (PANI)-based device switching potential control by adding nucleotide-free analogue of vitamin B12, aquacyanocobinamide, to the electrolyte solution. The employed concentrations of this molecule, ranging from 0.2 to 2 mM, enabled organic memristive devices to achieve switching potential decrease for up to 100 mV, thus providing a way to control device properties. This effect is attributed to strong aromatic interactions between PANI phenyl groups and corrin macrocycle of the aquacyanocobinamide molecule, which was supported by ultraviolet-visible spectra analysis.

Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO2-MoS2 Core-Shell Heterostructures for Future Bio-Inspired Computing

Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.

Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

Intelligent perceptual textiles based on ionic-conductive and strong silk fibers

Endowing textiles with perceptual function, similar to human skin, is crucial for the development of next-generation smart wearables. To date, the creation of perceptual textiles capable of sensing potential dangers and accurately pinpointing finger touch remains elusive. In this study, we present the design and fabrication of intelligent perceptual textiles capable of electrically responding to external dangers and precisely detecting human touch, based on conductive silk fibroin-based ionic hydrogel (SIH) fibers. These fibers possess excellent fracture strength (55 MPa), extensibility (530%), stable and good conductivity (0.45 S·m-1) due to oriented structures and ionic incorporation. We fabricated SIH fiber-based protective textiles that can respond to fire, water, and sharp objects, protecting robots from potential injuries. Additionally, we designed perceptual textiles that can specifically pinpoint finger touch, serving as convenient human-machine interfaces. Our work sheds new light on the design of next-generation smart wearables and the reshaping of human-machine interfaces.

Antiplasmodial potential of phytochemicals from Citrus aurantifolia peels: a comprehensive in vitro and in silico study

Phytochemical investigation of Key lime (Citrus aurantifolia L., F. Rutaceae) peels afforded six metabolites, known as methyl isolimonate acetate (1), limonin (2), luteolin (3), 3`-hydroxygenkwanin (4), myricetin (5), and europetin (6). The structures of the isolated compounds were assigned by 1D NMR. In the case of limonin (2), further 1- and 2D NMR experiments were done to further confirm the structure of this most active metabolite. The antiplasmodial properties of the obtained compounds against the pathogenic NF54 strain of Plasmodium falciparum were assessed in vitro. According to antiplasmodial screening, only limonin (2), luteolin (3), and myricetin (5) were effective (IC50 values of 0.2, 3.4, and 5.9 µM, respectively). We explored the antiplasmodial potential of phytochemicals from C. aurantifolia peels using a stepwise in silico-based analysis. We first identified the unique proteins of P. falciparum that have no homolog in the human proteome, and then performed inverse docking, ΔGBinding calculation, and molecular dynamics simulation to predict the binding affinity and stability of the isolated compounds with these proteins. We found that limonin (2), luteolin (3), and myricetin (5) could interact with 20S a proteasome, choline kinase, and phosphocholine cytidylyltransferase, respectively, which are important enzymes for the survival and growth of the parasite. According to our findings, phytochemicals from C. aurantifolia peels can be considered as potential leads for the development of new safe and effective antiplasmodial agents.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

Novel Three-Dimensional Artificial Neural Network Based on an Eight-Layer Vertical Memristor with an Ultrahigh Rectify Ratio (>107) and an Ultrahigh Nonlinearity (>105) for Neuromorphic Computing

In recent years, memristors have successfully demonstrated their significant potential in artificial neural networks (ANNs) and neuromorphic computing. Nonetheless, ANNs constructed by crossbar arrays suffer from cross-talk issues and low integration densities. Here, we propose an eight-layer three-dimensional (3D) vertical crossbar memristor with an ultrahigh rectify ratio (RR > 107) and an ultrahigh nonlinearity (>105) to overcome these limitations, which enables it to reach a >1 Tb array size without reading failure. Furthermore, the proposed 3D RRAM shows advanced endurance (>1010 cycles), retention (>104 s), and uniformity. In addition, several synaptic functions observed in the human brain were mimicked. On the basis of the advanced performance, we constructed a novel 3D ANN, whose learning efficiency and recognition accuracy were enhanced significantly compared with those of conventional single-layer ANNs. These findings hold promise for the development of highly efficient, precise, integrated, and stable VLSI neuromorphic computing systems.

Self-Rectifying All-Optical Modulated Optoelectronic Multistates Memristor Crossbar Array for Neuromorphic Computing

Researching optoelectronic memristors capable of integrating sensory and processing functions is essential for advancing the development of efficient neuromorphic vision. Here, we experimentally demonstrated an all-optical controlled and self-rectifying optoelectronic memristor (OEM) crossbar array with the function of multilevel storage under light stimuli. The NiO/TiO2 device exhibits an ultrahigh (>104) rectifying ratio (RR) thus overcoming the presence of sneak current. The reversible conductance modulation without electric signal involvement provides a novel way to realize ultrafast information processing. The proposed OEM array realized synaptic functions observed in the human brain, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), the transition from short-term memory (STM) to long-term memory (LTM), and learning experience behaviors successfully. The authors present a novel OEM crossbar that possesses complete light-modulation capabilities, potentially advancing the future development of efficient neuromorphic vision.

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

Low-power and high-speed HfLaO-based FE-TFTs for artificial synapse and reconfigurable logic applications

Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.

Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin

The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104  s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.