Stretchable and conformable synapse memristors for wearable and implantable electronics

An Ultrasensitive Biomimetic Optic Afferent Nervous System with Circadian Learnability

The optic afferent nervous system (OANS) plays a significant role in generating vision and circadian behaviors based on light detection and signals from the endocrine system. However, the bionic simulation of this photochemically mediated behavior is still a challenge for neuromorphic devices. Herein, stimuli of neurotransmitters at ultralow concentrations and illumination are coupled to artificial synapses with the aid of biofunctionalized heterojunction and tunneling to successfully simulate a circadian neural response. Furthermore, the mechanisms underlying the photosensitive synaptic current in response to stimuli are described. Interestingly, this OANS is demonstrated to be capable of mimicking normal and abnormal circadian learnability by combining the measured synaptic current with a three-layer spike neural network. Strong theoretical and experimental evidence, as well as applications, are provided for the proposed biomimetic OANS to demonstrate that it can reproduce biological circadian behavior, thus establishing it as a promising candidate for future neuromorphic intelligent robots.

A reversible implantable memristor for health monitoring applications

Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing and low power consumption offer a promising solution to overcome the computational bottleneck and Moore's law limitations of traditional silicon-based implantable devices, making them the most promising candidates for next-generation implantable devices. In this work, a highly stable memristor with an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles and endurance above 1000 s. The device successfully exhibited three-stage responses to biological signals after implantation in SD (Sprague-Dawley) rats. Importantly, the memristor perform remarkable reversibility, maintaining over 100 cycles of stable repetition even after extraction from the rat. This study provides a new perspective on the biomedical application of memristors, expanding the potential of implantable memristive devices in intelligent medical fields such as health monitoring and auxiliary diagnostics.

Stretchable and neuromorphic transistors for pain perception and sensitization emulation

Pain perception nociceptors (PPN), an important type of sensory neuron, are capable of sending out alarm signals when the human body is exposed to destructive stimuli. Simulating the human ability to perceive the external environment and spontaneously avoid injury is a critical function of neural sensing of artificial intelligence devices. The demand for developing artificial PPN has subsequently increased. However, due to the application scenarios of bionic electronic devices such as human skin, electronic prostheses, and robot bodies, where a certain degree of surface deformation constantly occurs, the ideal artificial PPN should have the stretchability to adapt to real scenarios. Here, an organic semiconductor nanofiber artificial pain perception nociceptor (NAPPN) based on a pre-stretching strategy is demonstrated to achieve key pain aspects such as threshold, sensitization, and desensitization. Remarkably, while stretching up to 50%, the synaptic behaviors and injury warning ability of NAPPN can be retained. To verify the wearability of the device, NAPPN was attached to a curved human finger joint, on which PPN behaviors were successfully mimicked. This provides a promising strategy for realizing neural sensing function on either deformed or mobile electronic devices.

Liquid Metal Memory

Storage systems are vital components of electronic devices, while significant challenges persist in achieving flexible memory due to the limitations of existing storage methodologies. Inspired by the polarization and depolarization mechanisms in the human brain, here a novel class of storage principles is proposed and achieve a fully flexible memory through introducing the oxidation and deoxidation behaviors of liquid metals. Specifically, reversible electrochemical oxidation is utilized to modulate the overall conductivity of the target liquid metals, creating a substantial 11-order resistance difference for binary data storage. To obtain the best storage performance, systematic optimizations of multiple parameters are conducted. Conceptual experiments demonstrate the memory's stability under extreme deformations (100% stretching, 180° bending, 360° twisting). Further tests reveal that the memory performs better when its unit size gets smaller, warranting superior integrability. Finally, a complete storage system achieves remarkable performance metrics, including rapid storage speed (>33 Hz), long data retention capacity (>43200 s), and stable repeatable operation (>3500 cycles). This groundbreaking method not only overcomes the inherent rigidity limitations of existing electronic storage units but also opens new possibilities for innovating neuromorphic devices, offering fundamental and practical avenues for future applications in soft robotics, wearable electronics, and bio-inspired artificial intelligence systems.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element

Neuromorphic devices are a promising alternative to the traditional von Neumann architecture. These devices have the potential to achieve high-speed, efficient, and low-power artificial intelligence. Flexibility is required in these devices so that they can bend and flex without causing damage to the underlying electronics. This feature shows a possible use in applications that require flexible electronics, such as robotics and wearable electronics. Here, we report a flexible self-formed Ag-based neuromorphic device that emulates various brain-inspired synaptic activities, such as short-term plasticity and long-term potentiation (STP and LTP) in both the flat and bent states. Half and full-integer quantum conductance jumps were also observed in the flat and bent states. The device showed excellent switching and endurance behaviors. The classical conditioning could be emulated even in the bent state.

Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics

The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2  V-1  s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.

Controllable modulation of the oxygen vacancy-induced adjustment of memristive behavior for direct differential operation with transistor-free memristor

To achieve the goal of neuromorphic computing hardware implementation with extremely high efficiency, low power consumption, and high density, it is necessary to develop transistor-free memristors and implement differential operation without subtraction circuits. In this study, argon ion irradiation was used during the fabrication process of a single crystalline LiNbO3 (LN) thin film to controllably introduce oxygen vacancies (OVs) into the bottom surface, which realized the modulation of OVs based on the excellent environment provided by a single-crystalline thin film. The memristive behavior of memristors was then modulated by regulating the distribution of OVs, and the effect of OVs distributed near the bottom surface of the single crystalline LN thin film on the memristive behavior was analyzed. In this way, two transistor-free memristors with opposite memristive behavior directions were fabricated. Two transistor-free memristors exhibit excellent synaptic plasticity and reliable multilevel resistance states. Based on two transistor-free memristors, a novel differential pair was constructed. Hardware implementations of direct differential operation without subtraction circuits were achieved. This study provides a new pathway to develop a transistor-free memristor and achieve differential operation without subtraction circuits in neuromorphic computing, which will simplify the peripheral circuits, improve integration density, and reduce power consumption and latency.

Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe2 Enable Uniform and Fast Memristive Switching

Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe₂ material and then oxidized it into TiO₂ in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe₂/TiO₂/TiSe₂ device exhibits excellent resistive switching performance with a fairly low threshold voltage (V_set < 0.8 V, V_reset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.

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.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix

The conductive filament (CF) model, as an important means to realize synaptic functions, has received extensive attention and has been the subject of intense research. However, the random and uncontrollable growth of CFs seriously affects the performances of such devices. In this work, we prepared a neural synaptic device based on polyvinyl pyrrolidone-molybdenum disulfide quantum dot (MoS₂ QD) nanocomposites. The doping with MoS₂ QDs was found to control the growth mode of Ag CFs by providing active centers for the formation of Ag clusters, thus reducing the uncertainty surrounding the growth of Ag CFs. As a result, the device, with a low power consumption of 32.8 pJ/event, could be used to emulate a variety of synaptic functions, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, post-tetanic potentiation, short-term memory to long-term memory conversion, and "learning experience" behavior. After having undergone consecutive stimulation with different numbers of pulses, the device stably realized a "multi-level LTP to LTD conversion" function. Moreover, the synaptic characteristics of the device experienced no degradation due to mechanical stress. Finally, the simulation result based on the synaptic characteristics of our devices achieved a high recognition accuracy of 91.77% in learning and inference tests and showed clear digital classification results.

Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing

Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO₃ doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10⁴), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO₃ is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.

Robust and Low-Power-Consumption Black Phosphorus-Graphene Artificial Synaptic Devices

Two-dimensional (2D) black phosphorus (BP) materials, as the most promising building blocks for the development of artificial synapses, have attracted more and more attention. However, the instability of exfoliated 2D BP structures still remains a problem in the development of artificial synapse devices. In this study, the robust and low-power-consumption artificial-synaptic-based BP was successfully manufactured. The synapse devices have high stability in the air atmosphere and do not show obvious degradation over 3 months. The obtained devices not only implement the main function of synapses but also perform the function of dendritic neural synapses and simple logical operations, revealing their very strong learning behavior. The high mobility of 2D BP as well as the coupled effect and quantum confinement effect of the graphene oxide quantum dot (GOQD) can greatly boost the performance of BP-based synapse devices, such as low power consumption (62 pW) and high sensitivity (ultrasmall stimuli at an amplitude of -20 mV). Moreover, benefiting from the GOQD and the interaction between BP and graphene, the main dominated mechanism of the BP-graphene synapse device can be the capture and release of electrons by the 2D BP and GOQD instead of the conductive filament.

Improvement of synaptic plasticity by nanoparticles and the related mechanisms: Applications and prospects

Synaptic plasticity is an important basis of learning and memory and participates in brain network remodelling after different types of brain injury (such as that caused by neurodegenerative diseases, cerebral ischaemic injury, posttraumatic stress disorder (PTSD), and psychiatric disorders). Therefore, improving synaptic plasticity is particularly important for the treatment of nervous system-related diseases. With the rapid development of nanotechnology, increasing evidence has shown that nanoparticles (NPs) can cross the blood-brain barrier (BBB) in different ways, directly or indirectly act on nerve cells, regulate synaptic plasticity, and ultimately improve nerve function. Therefore, to better elucidate the effect of NPs on synaptic plasticity, we review evidence showing that NPs can improve synaptic plasticity by regulating different influencing factors, such as neurotransmitters, receptors, presynaptic membrane proteins and postsynaptic membrane proteins, and further discuss the possible mechanism by which NPs improve synaptic plasticity. We conclude that NPs can improve synaptic plasticity and restore the function of damaged nerves by inhibiting neuroinflammation and oxidative stress, inducing autophagy, and regulating ion channels on the cell membrane. By reviewing the mechanism by which NPs regulate synaptic plasticity and the applications of NPs for the treatment of neurological diseases, we also propose directions for future research in this field and provide an important reference for follow-up research.

Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition

We fabricated a nanowire-channel intrinsically stretchable neuromorphic transistor (NISNT) that perceives both tactile and visual information and emulates neuromorphic processing capabilities. The device demonstrated excellent stretching endurance of 1000 stretch cycles while retaining stable electrical properties. The device was then applied as a multisensitive afferent nerve that processes information in parallel. Compatible with skin deformation, the devices are attached to fingers to serve as conformal strain sensors and neuromorphic information-processing units for gesture recognition. The excitatory postsynaptic current in each device represents shape changes and is then analyzed using softmax activation processing of the neural network to recognize gestures. A multistage neural network that uses NISNT was used to further confirm the gestures. This work demonstrated an idea toward multisensory artificial nerves and neuromorphic systems.

Flexible and Stretchable Memristive Arrays for in-Memory Computing

With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.

Research Progress of Biomimetic Memristor Flexible Synapse

With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.

Stretchable Transparent Electrode via Wettability Self-Assembly in Mechanically Induced Self-Cracking

Stretchable and transparent electrodes (STEs) are indispensable components in numerous emerging applications such as optoelectrical devices and wearable devices used in health monitoring, human-machine interaction, and artificial intelligence. However, STEs have limitations in conductivity, robustness, and transmittance owing to the exposure of the substrate and fatigue deformation of nanomaterials under strain. In this study, an STE consisting of conductive materials embedded in in situ self-cracking strain-spread channels by wettability self-assembly is fabricated. Finite element analysis is used to simulate the crevice growth using the representative unit cell network and strain deformation using a random network. The embedded conductive materials are partly protected by the strain-opening crevice channel, and network dissociation is avoided under stretching, showing a maximum strain of 125%, a transmittance of approximately 89.66% (excluding the substrate) with a square resistance of 9.8 Ω sq-1, and high stability in an environment with high temperature and moisture. The wettability self-assembly coating process is verified and expanded to several kinds of hydrophilic inks and hydrophobic coating materials. The fabricated STE can be employed as a strain sensor in motion sensing, vital sign and posture feedback, and mimicking bioelectronic spiderweb with spatial gravity induction.

Artificial Skin Perception

Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.

Flexible organic field-effect transistor arrays for wearable neuromorphic device applications

With the advent of wearable microelectronic devices in the interdisciplinary bio-electronics research field, synaptic devices with capability of neuromorphic computing are attracting more and more attention as the building blocks for the next generation computing structure. Conventional flash-like synaptic transistors are built on rigid solid-state substrates, and the inorganic materials and the high-temperature processing steps have severely limited their applications in various flexible electronic devices and systems. Here, flexible organic flash-like synaptic devices have been fabricated on a flexible substrate with the organic C8-BTBT as the conducting channel. The device exhibits a memory window greater than 20 V and excellent synaptic functions including short/long-term synaptic plasticity and spike-timing-dependent plasticity. In addition, even under the bending condition (7 mm bending radius), the transistor can still stably achieve a variety of synaptic functions. This work shows that low-temperature processing technology with the integration of organic materials can pave a promising pathway for the realization of flexible synaptic systems and the future development of wearable electronic devices.

Ultralow Power Wearable Heterosynapse with Photoelectric Synergistic Modulation

Although the energy consumption of reported neuromorphic computing devices inspired by biological systems has become lower than traditional memory, it still remains greater than bio-synapses (≈10 fJ per spike). Herein, a flexible MoS2-based heterosynapse is designed with two modulation modes, an electronic mode and a photoexcited mode. A one-step mechanical exfoliation method on flexible substrate and low-temperature atomic layer deposition process compatible with flexible electronics are developed for fabricating wearable heterosynapses. With a pre-spike of 100 ns, the synaptic device exhibits ultralow energy consumption of 18.3 aJ per spike in long-term potentiation and 28.9 aJ per spike in long-term depression. The ultrafast speed and ultralow power consumption provide a path for a neuromorphic computing system owning more excellent processing ability than the human brain. By adding optical modulation, a modulatory synapse is constructed to dynamically control correlations between pre- and post-synapses and realize complex global neuromodulations. The novel wearable heterosynapse expands the accessible range of synaptic weights (ratio of facilitation ≈228%), providing an insight into the application of wearable 2D highly efficient neuromorphic computing architectures.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications.

Recent Progress in Three-Terminal Artificial Synapses: From Device to System

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three-terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three-terminal artificial synapses and artificial sensory systems is reviewed.

A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors

Physically transient electronic devices that can disappear on demand have great application prospects in the field of information security, implantable biomedical systems, and environment friendly electronics. On the other hand, the memristor-based artificial synapse is a promising candidate for new generation neuromorphic computing systems in artificial intelligence applications. Therefore, a physically transient synapse based on memristors is highly desirable for security neuromorphic computing and bio-integrated systems. Here, this is the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) behaviors. Moreover, to emulate the apoptotic process of biological neurons, the transient synapse devices can be dissolved completely in phosphate-buffered saline solution (PBS) or deionized (DI) water in 7 min. This work opens the route to security neuromorphic computing for smart security and defense electronic systems, as well as for neuro-medicine and implantable electronic systems.