A Flexible Artificial Sensory Nerve Enabled by Nanoparticle-Assembled Synaptic Devices for Neuromorphic Tactile Recognition

Recent Progress on Heterojunction-Based Memristors and Artificial Synapses for Low-Power Neural Morphological Computing

Memristors and artificial synapses have attracted tremendous attention due to their promising potential for application in the field of neural morphological computing, but at the same time, continuous optimization and improvement in energy consumption are also highly desirable. In recent years, it has been demonstrated that heterojunction is of great significance in improving the energy consumption of memristors and artificial synapses. By optimizing the material composition, interface characteristics, and device structure of heterojunctions, energy consumption can be reduced, and performance stability and durability can be improved, providing strong support for achieving low-power neural morphological computing systems. Herein, we review the recent progress on heterojunction-based memristors and artificial synapses by summarizing the working mechanisms and recent advances in heterojunction memristors, in terms of material selection, structure design, fabrication techniques, performance optimization strategies, etc. Then, the applications of heterojunction-based artificial synapses in neuromorphological computing and deep learning are introduced and discussed. After that, the remaining bottlenecks restricting the development of heterojunction-based memristors and artificial synapses are introduced and discussed in detail. Finally, corresponding strategies to overcome the remaining challenges are proposed. We believe this review may shed light on the development of high-performance memristors and artificial synapse devices.

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.

Artificial Photothermal Nociceptor Using Mott Oscillators

Bioinspired sensory systems based on spike neural networks have received considerable attention in resolving high energy consumption and limited bandwidth in current sensory systems. To efficiently produce spike signals upon exposure to external stimuli, compact neuron devices are required for signal detection and their encoding into spikes in a single device. Herein, it is demonstrated that Mott oscillative spike neurons can integrate sensing and ceaseless spike generation in a compact form, which emulates the process of evoking photothermal sensing in the features of biological photothermal nociceptors. Interestingly, frequency-tunable and repetitive spikes are generated above the threshold value (Pth = 84 mW cm-2) as a characteristic of "threshold" in leaky-integrate-and-fire (LIF) neurons; the neuron devices successfully mimic a crucial feature of biological thermal nociceptors, including modulation of frequency coding and startup latency depending on the intensity of photothermal stimuli. Furthermore, Mott spike neurons are self-adapted after sensitization upon exposure to high-intensity electromagnetic radiation, which can replicate allodynia and hyperalgesia in a biological sensory system. Thus, this study presents a unique approach to capturing and encoding environmental source data into spikes, enabling efficient sensing of environmental sources for the application of adaptive sensory systems.

Ferroelectric/Electric-Double-Layer-Modulated Synaptic Thin Film Transistors toward an Artificial Tactile Perception System

Tactile sensation and recognition in the human brain are indispensable for interaction between the human body and the surrounding environment. It is quite significant for intelligent robots to simulate human perception and decision-making functions in a more human-like way to perform complex tasks. A combination of tactile piezoelectric sensors with neuromorphic transistors provides an alternative way to achieve perception and cognition functions for intelligent robots in human-machine interaction scenarios. To promote both long-term and short-term plasticity of the artificial synaptic transistor, a composite gate dielectric composed of ferroelectric terpolymer P(VDF-TrFE-CFE) and chitosan was intendedly developed, while amorphous metal oxide InZnO was adopted as the channel layer. The transition from short-term to long-term plasticity function was realized on the basis of the electric-double-layer effect and ferroelectric polarization. Benefiting from its low-voltage operation performance, this synaptic transistor was functionalized by connecting with a flexible piezoelectric poly(vinylidene fluoride) capacitor to exhibit tactile stimulus-excited synaptic behavior. Feedback control was further introduced into the tactile synaptic system to imitate two typical scenarios of sensation and response, including the action of a mechanical claw to pain sensation and spontaneous scratching to itch sensation. This work provides a perspective on achieving intelligent perception for soft robotics and healthcare application.

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.

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.

Ultrasensitive Flexible Organic Synaptic Transistors Modulated by a Chemically Cross-Linked Solvent-Resistive Ion Composite

We demonstrate a flexible organic synaptic transistor (FOST) with an ion-composite electrolyte film resistant to chemical reagents, which uses a three-dimensionally cross-linked polyimide matrix to accommodate a high-concentration ionic liquid. FOST shows versatile synaptic plasticity for classical conditioning, high-pass filtering, and the learning-forgetting process. The device achieves low-energy consumption down to 1.02 femtojoule per synaptic event with an ultrasensitive impulse to presynaptic spike down to 0.5 mV. Moreover, the electrical performance of the device is still stable after 1000 mechanical bending cycles. These results demonstrate that the device can be applied to future flexible neuromorphic electronics.

Electro-Optically Configurable Synaptic Transistors With Cluster-Induced Photoactive Dielectric Layer for Visual Simulation and Biomotor Stimuli

The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

A Multimodal Perception-Enabled Flexible Memristor with Combined Sensing-Storage-Memory Functions for Enhanced Artificial Injury Recognition

With the continuous advancement of wearable technology and advanced medical monitoring, there is an increasing demand for electronic devices that can adapt to complex environments and have high perceptual sensitivity. Here, a novel artificial injury perception device based on an Ag/HfOx/ITO/PET flexible memristor is designed to address the limitations of current technologies in multimodal perception and environmental adaptability. The memristor exhibits excellent resistive switching (RS) performance and mechanical flexibility under different bending angles (BAs), temperatures, humid environment, and repetitive folding conditions. Further, the device demonstrates the multimodal perception and conversion capabilities toward voltage, mechanical, and thermal stimuli through current response tests under different conditions, enabling not only the simulation of artificial injury perception but also holds promise for monitoring and controlling the movement of robotic arms. Moreover, the logical operation capability of the memristor-based reconfigurable logic (MRL) gates is also demonstrated, proving the device has great potential applications with sensing, storage, and memory functions. Overall, this study not only provides a direction for the development of the next-generation flexible multimodal sensors, but also has significant implications for technological advancements in many fields such as robotic arms, electronic skin (e-skin), and medical monitoring.

Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor's capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

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.

A high-performance organic lithium salt-doped OFET with the optical radical effect for photoelectric pulse synaptic simulation and neuromorphic memory learning

Simulation of synaptic characteristics is essential for the application of organic field effect transistors (OFETs) in neural morphology. Although excellent performance, including bias stability and mobility, as well as photoelectric pulse synaptic simulation, has been achieved in SiO2-gated OFETs with PDVT-10 as an organic channel, there are relatively few studies on photoelectric pulse synaptic simulation of electrolyte-gated OFETs based on environmentally friendly and low-voltage operation. Herein, synaptic transistors based on organic semiconductors are reported to simulate the photoelectric pulse response by developing solution-based organic semiconductor PDVT-10, and polyvinyl alcohol (PVA) with an electric double layer (EDL) effect to act as a channel and gate dielectric layer, respectively, and organic lithium salt-doped PVA is used to enhance the EDL effect. The presence of electrical pulses in doped devices not only achieves basic electrical synaptic characteristics, but also significantly realizes the long-term characteristics, pain perception, memory and sensitization applications. Furthermore, the introduction of photoinitiator molecules into the channel layer leads to improved photosynaptic performances by using light-induced free radicals, and the photoelectric synergistic effect has been actualized by introducing heterojunction architecture. This work provides promising prospects for achieving photoelectric pulse modulation based on organic synaptic devices, which shows great potential for the development of artificial intelligence.

Adaptive Processing Enabled by Sodium Alginate Based Complementary Memristor for Neuromorphic Sensory System

Adaptive processing allows sensory systems to autonomically adjust their sensitivity with exposure to a constant sensory stimulus and thus organisms to adapt to environmental variations. Bioinspired electronics with adaptive functions are highly desirable for the development of neuromorphic sensory systems (NSSs). Herein, the functions of desensitization and sensitivity changing with background intensity (i.e., Weber's law), as two fundamental cues of sensory adaptation, are biorealistically demonstrated in an Ag nanowire (NW)-embedded sodium alginate (SA) based complementary memristor. In particular, Weber's law is experimentally emulated in a single complementary memristor. Furthermore, three types of adaptive NSS unit are constructed to realize a multiple perceptual capability that processes the stimuli of illuminance, temperature, and pressure signals. Taking neuromorphic vision as an example, scotopic and photopic adaptation functions are well reproduced for image enhancement against dark and bright backgrounds. Importantly, an NSS system with multisensory integration function is demonstrated by combining light and pressure spikes, where the accuracy of pattern recognition is obviously enhanced relative to that of an individual sense. This work offers a new strategy for developing neuromorphic electronics with adaptive functions and paves the way toward developing a highly efficient NSS.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

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.

Spatial evolution of the proton-coupled Mott transition in correlated oxides for neuromorphic computing

The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO3-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Neuromorphic antennal sensory system

Insect antennae facilitate the nuanced detection of vibrations and deflections, and the non-contact perception of magnetic or chemical stimuli, capabilities not found in mammalian skin. Here, we report a neuromorphic antennal sensory system that emulates the structural, functional, and neuronal characteristics of ant antennae. Our system comprises electronic antennae sensor with three-dimensional flexible structures that detects tactile and magnetic stimuli. The integration of artificial synaptic devices adsorbed with solution-processable MoS2 nanoflakes enables synaptic processing of sensory information. By emulating the architecture of receptor-neuron pathway, our system realizes hardware-level, spatiotemporal perception of tactile contact, surface pattern, and magnetic field (detection limits: 1.3 mN, 50 μm, 9.4 mT). Vibrotactile-perception tasks involving profile and texture classifications were accomplished with high accuracy (> 90%), surpassing human performance in "blind" tactile explorations. Magneto-perception tasks including magnetic navigation and touchless interaction were successfully completed. Our work represents a milestone for neuromorphic sensory systems and biomimetic perceptual intelligence.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

Ion trap and release dynamics enables nonintrusive tactile augmentation in monolithic sensory neuron

An iontronic-based artificial tactile nerve is a promising technology for emulating the tactile recognition and learning of human skin with low power consumption. However, its weak tactile memory and complex integration structure remain challenging. We present an ion trap and release dynamics (iTRD)-driven, neuro-inspired monolithic artificial tactile neuron (NeuroMAT) that can achieve tactile perception and memory consolidation in a single device. Through the tactile-driven release of ions initially trapped within iTRD-iongel, NeuroMAT only generates nonintrusive synaptic memory signals when mechanical stress is applied under voltage stimulation. The induced tactile memory is augmented by auxiliary voltage pulses independent of tactile sensing signals. We integrate NeuroMAT with an anthropomorphic robotic hand system to imitate memory-based human motion; the robust tactile memory of NeuroMAT enables the hand to consistently perform reliable gripping motion.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Adaptive Memory of a Neuromorphic Transistor with Multi-Sensory Signal Fusion

One of the ultimate goals of artificial intelligence is to achieve the capability of memory evolution and adaptability to changing environments, which is termed adaptive memory. To realize adaptive memory in artificial neuromorphic devices, artificial synapses with multi-sensing capability are required to collect and analyze various sensory cues from the external changing environments. However, due to the lack of platforms for mediating multiple sensory signals, most artificial synapses have been mainly limited to unimodal or bimodal sensory devices. Herein, we present a multi-modal artificial sensory synapse (MASS) based on an organic synapse to realize sensory fusion and adaptive memory. The MASS receives optical, electrical, and pressure information and in turn generates typical synaptic behaviors, mimicking the multi-sensory neurons in the brain. Sophisticated synaptic behaviors, such as Pavlovian dogs, writing/erasing, signal accumulation, and offset, were emulated to demonstrate the joint efforts of bimodal sensory cues. Moreover, associative memory can be formed in the device and be subsequently reshaped by signals from a third perception, mimicking modification of the memory and cognition when encountering a new environment. Our MASS provides a step toward next-generation artificial neural networks with an adaptive memory capability.

High sensitivity and wide response range artificial synapse based on polyimide with embedded graphene quantum dots

Artificial electronic synapses are commonly used to simulate biological synapses to realize various learning functions, regarded as one of the key technologies in the next generation of neurological computation. This work used a simple spin coating technique to fabricate polyimide (PI):graphene quantum dots(GQDs) memristor structure. As a result, the devices exhibit remarkably stable exponentially decaying postsynaptic suppression current over time, as interpreted in the spike-timing-dependent plasticity phenomenon. Furthermore, with the increase of the applied electrical signal over time, the conductance of the electrical synapse gradually changes, and the electronic synapse also shows plasticity dependence on the amplitude and frequency of the pulse applied. In particular, the devices with the structure of Ag/PI:GQDs/ITO prepared in this study can produce a stable response to the stimulation of electrical signals between millivolt to volt, showing not only high sensitivity but also a wide range of "feelings", which makes the electronic synapses take a step forwards to emulate biological synapses. Meanwhile, the electronic conduction mechanisms of the device are also studied and expounded in detail. The findings in this work lay a foundation for developing brain-like neuromorphic modeling in artificial intelligence.

Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.

Doppler Frequency-Shift Information Processing in WOx -Based Memristive Synapse for Auditory Motion Perception

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency-shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WO_x -based memristive synapse. The WO_x memristor presents both the volatile mode (M1) and semi-nonvolatile mode (M2), which are capable of implementing the high-pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency-shift information processing for velocity detection is emulated in the WO_x memristor based auditory system for the first time, which relies on a scheme of triplet spike-timing-dependent-plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.

Ligand-Triggered Self-Assembly of Flexible Carbon Dot Nanoribbons for Optoelectronic Memristor Devices and Neuromorphic Computing

Carbon dots (CDs) are widely utilized in sensing, energy storage, and catalysis due to their excellent optical, electrical and semiconducting properties. However, attempts to optimize their optoelectronic performance through high-order manipulation have met with little success to date. In this study, through efficient packing of individual CDs in two-dimensions, the synthesis of flexible CDs ribbons is demonstrated technically. Electron microscopies and molecular dynamics simulations, show the assembly of CDs into ribbons results from the tripartite balance of π-π attractions, hydrogen bonding, and halogen bonding forces provided by the superficial ligands. The obtained ribbons are flexible and show excellent stability against UV irradiation and heating. CDs ribbons offer outstanding performance as active layer material in transparent flexible memristors, with the developed devices providing excellent data storage, retention capabilities, and fast optoelectronic responses. A memristor device with a thickness of 8 µm shows good data retention capability even after 10⁴ cycles of bending. Furthermore, the device functions effectively as a neuromorphic computing system with integrated storage and computation capabilities, with the response speed of the device being less than 5.5 ns. These properties create an optoelectronic memristor with rapid Chinese character learning capability. This work lays the foundation for wearable artificial intelligence.

Coaxial Graphene/MXene Microfibers with Interfacial Buffer-Based Lightweight Distance Sensors Assisting Lossless Grasping of Fragile and Deformable Objects

Lossless and efficient robotic grasping is becoming increasingly important with the widespread application of intelligent robotics in warehouse transportation, human healthcare, and domestic services. However, current sensors for feedback of grasping behavior are greatly restricted by high manufacturing cost, large volume and mass, complex circuit, and signal crosstalk. To solve these problems, here, we prepare lightweight distance sensor-based reduced graphene oxide (rGO)/MXene-rGO coaxial microfibers with interface buffer to assist lossless grasping of a robotic manipulator. The as-fabricated distance microsensor exhibits a high sensitivity of 91.2 m^-1 in the distance range of 50-300 μm, a fast response time of 116 ms, a high resolution of 5 μm, and good stability in 500 cycles. Furthermore, the high-performance and lightweight microsensor is installed on the robotic manipulator to reflect the grasp state by the displacement imposed on the sensor. By establishing the correlation between the microsensing signal and the grasp state, the safe, non-destructive, and effective grasp and release of the target can be achieved. The lightweight and high-powered distance sensor displays great application prospects in intelligent fetching, medical surgery, multi-spindle automatic machines, and cultural relics excavation.

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

Emerging electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

Magneto-Tactile Sensor Based on a Commercial Polyurethane Sponge

In this paper, we present the procedure for fabricating a new magneto-tactile sensor (MTS) based on a low-cost commercial polyurethane sponge, including the experimental test configuration, the experimental process, and a description of the mechanisms that lead to obtaining the MTS and its characteristics. It is shown that by using a polyurethane sponge, microparticles of carbonyl iron, ethanol, and copper foil with electroconductive adhesive, we can obtain a high-performance and low-cost MTS. With the experimental assembly described in this paper, the variation in time of the electrical capacity of the MTS was measured in the presence of a deforming force field, a magnetic field, and a magnetic field superimposed over a deformation field. It is shown that, by using an external magnetic field, the sensitivity of the MTS can be increased. Using the magnetic dipole model and linear elasticity approximation, the qualitative mechanisms leading to the reported results are described in detail.