Contact-electrification-activated artificial afferents at femtojoule energy

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.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.

Toward a Brain-Neuromorphics Interface

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

Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (-0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.

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.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Self-adaptive rotational electromagnetic energy generation as an alternative to triboelectric and piezoelectric transductions

Triboelectric and piezoelectric energy harvesters can hardly power most microelectronic systems. Rotational electromagnetic harvesters are very promising alternatives, but their performance is highly dependent on the varying mechanical sources. This study presents an innovative approach to significantly increase the performance of rotational harvesters, based on dynamic coil switching strategies for optimization of the coil connection architecture during energy generation. Both analytical and experimental validations of the concept of self-adaptive rotational harvester were carried out. The adaptive harvester was able to provide an average power increase of 63.3% and 79.5% when compared to a non-adaptive 16-coil harvester for harmonic translation and harmonic swaying excitations, respectively, and 83.5% and 87.2% when compared to a non-adaptive 8-coil harvester. The estimated energy conversion efficiency was also enhanced from ~80% to 90%. This study unravels an emerging technological approach to power a wide range of applications that cannot be powered by other vibrationally driven harvesters.

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.

Utilizing a High-Performance Piezoelectric Nanocomposite as a Self-Activating Component in Piezotronic Artificial Mechanoreceptors

Challenges such as poor dispersion and insufficient polarization of BaTiO3 (BTO) nanoparticles (NPs) within poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) composites have hindered their piezoelectricity, limiting their uses in pressure sensors, nanogenerators, and artificial sensory synapses. Here, we introduce a high-performance piezoelectric nanocomposite material consisting of P(VDF-TrFE)/modified-BTO (mBTO) NPs for use as a self-activating component in a piezotronic artificial mechanoreceptor. To generate high-performance piezoelectric nanocomposite materials, the surface of BTO is hydroxylated, followed by the covalent attachment of (3-aminopropyl)triethoxysilane to improve the dispersibility of mBTO NPs within the P(VDF-TrFE) matrix. We also aim to enhance the crystallization degree of P(VDF-TrFE), the efficiency characteristics of mBTO, and the poling efficiency, even when incorporating small amounts of mBTO NPs. The piezoelectric potential mechanically induced from the P(VDF-TrFE)/mBTO NPs nanocomposite was three times greater than that from P(VDF-TrFE) and twice as high as that from the P(VDF-TrFE)/BTO NPs nanocomposite. The piezoelectric potential generated by mechanical stimuli on the piezoelectric nanocomposite was utilized to activate the synaptic ionogel-gated field-effect transistor for the development of self-powered piezotronics artificial mechanoreceptors on a polyimide substrate. The device successfully emulated fast-adapting (FA) functions found in biological FA mechanoreceptors. This approach has great potential for applications to future intelligent tactile perception technology.

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.

Recent Progress of Solid-Liquid Interface-Mediated Contact-Electro-Catalysis

Contact electrification (CE) is a common physical process by which triboelectric charges are generated through the mutual contact between two objects. Despite the ongoing debates on CE's mechanism, recent advancements in technology have elucidated the primary role of electron transfer in most CE processes. This discovery leads to the spawning of an emerging field, known as contact-electro-catalysis (CEC), which utilizes the electron transfer phenomenon during CE to initiate CEC. In this work, we provide the first comprehensive review of the recent progress of the solid-liquid interface-mediated CEC process, including its working principles, relationship with surface science, recent breakthroughs in applications, and future challenges. We aim to provide fundamental guidance for researchers to understand the reaction mechanism of the CEC process and to propose potential pathways to enhance CEC efficiency from a surface and interfacial science perspective. Later, recent application scenarios using the novel CEC techniques are summarized, including wastewater treatment, efficient generation of hydrogen peroxide (H2O2), lithium-ion battery recycling, and CO2 reduction. In general, CEC technology has opened a new avenue for catalysis, effectively expanding the range of catalyst options and holding promise as a solution to a variety of complex catalytic challenges in the future.

Sustainable paper electronics and neuromorphic paper chip

Paper electronics have received a lot of attention due to their special properties of mechanical flexibility/foldability, sustainability, biodegradability, light weight, and low cost. It provides a superb on-chip prototype with simple modular design and feasible energy-autonomous features, which can surpass the problems of inconvenience and possible pollution caused by conventional power sources by integrating different functional modules. Commonly, the sustainable operation of integrated paper electronics can be guaranteed by the basic components, including energy-harvesting devices, energy-storage devices, and low-power-consuming functional circuits/devices. Furthermore, sustainable paper electronics are possible to be further extended to develop energy-efficient neuromorphic paper chip by utilizing cutting-edge neuromorphic components based on traditional paper-based transistors, memories, and logic gates toward potential in-memory computing applications. The working process of the sustainable paper electronics implies an energy cycling of surrounding energy conversion, electrochemical energy storage, and energy utilization in functional circuits (in the form of photonic, thermal, electromagnetic, or mechanical energy). Sustainable paper electronics provide a promising path for achieving efficient, cost-effective, and customizable integrated electronics and self-powered systems with complementary features.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Energy-efficient electronics with an air-friction-driven rotating gate transistor using tribotronics

Concern for the environment is one of the main factors that are increasing the demand for compact and energy-efficient electronic devices. Recent research has made advances in reducing the power consumption of field-effect transistors, including the use of high-dielectric insulators, low-voltage operation, and selective power-conservation strategies. This paper introduces a revolutionary air-friction-driven rotating gate transistor that operates without the need for a conventional gate voltage. This new device offers the advantages of wear resistance, a slim and flexible design (achieved through low-temperature solution processing), and a simplified three-layer structure that streamlines manufacturing and reduces potential carbon emissions. This device's wear resistance and ease of fabrication render the device a promising technology with applications in various fields, including electronics, vehicles, aviation, and wearable devices. This study provides evidence of the device's feasibility for use in real-world vehicular scenarios, underscoring its potential for future innovation and widespread adoption.

Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks

The artificial tactile perception system of this work utilizes a fully connected spiking neural network (SNN) comprising two layers. Its architecture is streamlined and energy-efficient as it directly integrates spiking tactile neurons with piezoresistive sensors and Pt/NbOx/TiN memristors as input neurons. These spiking tactile neurons possess the ability to perceive and integrate pressure stimuli from multiple sensors and encode the information into rate-coded electrical spikes, closely resembling the behavior of a biological tactile neuron. The system's real-time information processing capability is demonstrated through an artificial perceptual learning system that successfully encodes and decodes the Morse code; the artificial perceptual learning system accurately recognizes and displays 26 English letters. Furthermore, the artificial tactile perception system is evaluated for the recognition of the MNIST data set, achieving a classification accuracy of 85.7% with the supervised spiking-rate-dependent plasticity learning rule. The key advantages of this artificial tactile perception system are its simple structure and high efficiency, which contributes to its practicality for various real-world applications.

Biomaterials and bioelectronics for self-powered neurostimulation

Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged as a compelling approach to explore, repair, and modulate neural systems. This review examines the application of self-powered bioelectronics for electrical stimulation of both the central and peripheral nervous systems, as well as isolated neurons. Contemporary research has adeptly harnessed biomechanical and biochemical energy from the human body, through various mechanisms such as triboelectricity, piezoelectricity, magnetoelasticity, and biofuel cells, to power these advanced bioelectronics. Notably, these self-powered bioelectronics hold substantial potential for delivering neural stimulations that are customized for the treatment of neurological diseases, facilitation of neural regeneration, and the development of neuroprosthetics. Looking ahead, we expect that the ongoing advancements in biomaterials and bioelectronics will drive the field of self-powered neurostimulation toward the realization of more advanced, closed-loop therapeutic solutions, paving the way for personalized and adaptable neurostimulators in the coming decades.

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.

Multi-Layered Triboelectric Nanogenerators with Controllable Multiple Spikes for Low-Power Artificial Synaptic Devices

In the domains of wearable electronics, robotics, and the Internet of Things, there is a demand for devices with low power consumption and the capability of multiplex sensing, memory, and learning. Triboelectric nanogenerators (TENGs) offer remarkable versatility in this regard, particularly when integrated with synaptic transistors that mimic biological synapses. However, conventional TENGs, generating only two spikes per cycle, have limitations when used in synaptic devices requiring repetitive high-frequency gating signals to perform various synaptic plasticity functions. Herein, a multi-layered micropatterned TENG (M-TENG) consisting of a polydimethylsiloxane (PDMS) film and a composite film that includes 1H,1H,2H,2H-perfluorooctyltrichlorosilane/BaTiO3 /PDMS are proposed. The M-TENG generates multiple spikes from a single touch by utilizing separate triboelectric charges at the multiple friction layers, along with a contact/separation delay achieved by distinct spacers between layers. This configuration allows the maximum triboelectric output charge of M-TENG to reach up to 7.52 nC, compared to 3.69 nC for a single-layered TENG. Furthermore, by integrating M-TENGs with an organic electrochemical transistor, the spike number multiplication property of M-TENGs is leveraged to demonstrate an artificial synaptic device with low energy consumption. As a proof-of-concept application, a robotic hand is operated through continuous memory training under repeated stimulations, successfully emulating long-term plasticity.

Unraveling Spatiotemporal Transient Dynamics at the Nanoscale via Wavelet Transform-Based Kelvin Probe Force Microscopy

Mechanistic probing of surface potential changes arising from dynamic charge transport is the key to understanding and engineering increasingly complex nanoscale materials and devices. Spatiotemporal averaging in conventional heterodyne detection-based Kelvin probe force microscopy (KPFM) inherently limits its time resolution, causing an irretrievable loss of transient response and higher-order harmonics. Addressing this, we report a wavelet transform (WT)-based methodology capable of quantifying the sub-ms charge dynamics and probing the elusive transient response. The feedback-free, open-loop wavelet transform KPFM (OL-WT-KPFM) technique harnesses the WT's ability to simultaneously extract spatial and temporal information from the photodetector signal to provide a dynamic mapping of surface potential, capacitance gradient, and dielectric constant at a temporal resolution 3 orders of magnitude higher than the lock-in time constant. We further demonstrate the method's applicability to explore the surface-photovoltage-induced sub-ms hole-diffusion transient in bismuth oxyiodide semiconductor. The OL-WT-KPFM concept is readily applicable to commercial systems and can provide the underlying basis for the real-time analysis of transient electronic and electrochemical properties.

Self-Powered Sensing in Wearable Electronics─A Paradigm Shift Technology

With the advancements in materials science and micro/nanoengineering, the field of wearable electronics has experienced a rapid growth and significantly impacted and transformed various aspects of daily human life. These devices enable individuals to conveniently access health assessments without visiting hospitals and provide continuous, detailed monitoring to create comprehensive health data sets for physicians to analyze and diagnose. Nonetheless, several challenges continue to hinder the practical application of wearable electronics, such as skin compliance, biocompatibility, stability, and power supply. In this review, we address the power supply issue and examine recent innovative self-powered technologies for wearable electronics. Specifically, we explore self-powered sensors and self-powered systems, the two primary strategies employed in this field. The former emphasizes the integration of nanogenerator devices as sensing units, thereby reducing overall system power consumption, while the latter focuses on utilizing nanogenerator devices as power sources to drive the entire sensing system. Finally, we present the future challenges and perspectives for self-powered wearable electronics.

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.

Recent Advanced Applications of Ionic Liquid for Future Iontronics

Recently, electronic devices that make use of a state called the electric double layers (EDL) of ion have opened up a wide range of research opportunities, from novel physical phenomena in solid-state materials to next-generation low-power consumption devices. They are considered to be the future iontronics devices. EDLs behave as nanogap capacitors, resulting the high density of charge carriers is induced at semiconductor/electrolyte by applying only a few volts of the bias voltage. This enables the low-power operation of electronic devices as well as new functional devices. Furthermore, by controlling the motion of ions, ions can be used as semi-permanent charge to form electrets. In this article, we are going to introduce the recent advanced application of iontronics devices as well as energy harvesters making use of ion-based electrets, leading to the future iontronics research.

Skin-Inspired Highly Sensitive Tactile Sensors with Ultrahigh Resolution over a Broad Sensing Range

Flexible tactile sensors with high sensitivity, a broad pressure detection range, and high resolution are highly desired for the applications of health monitoring, robots, and the human-machine interface. However, it is still challenging to realize a tactile sensor with high sensitivity and resolution over a wide detection range. Herein, to solve the abovementioned problem, we demonstrate a universal route to develop a highly sensitive tactile sensor with high resolution and a wide pressure range. The tactile sensor is composed of two layers of microstructured flexible electrodes with high modulus and conductive cotton fabric with low modulus. By optimizing the sensing films, the fabricated tactile sensor shows a high sensitivity of 8.9 × 10⁴ kPa^-1 from 2 Pa to 250 kPa because of the high structural compressibility and stress adaptation of the multilayered composite films. Meanwhile, a fast response speed of 18 ms, an ultrahigh resolution of 100 Pa over 100 kPa, and excellent durability over 20 000 loading/unloading cycles are demonstrated. Moreover, a 6 × 6 tactile sensor array is fabricated and shows promising potential application in electronic skin (e-skin). Therefore, employing multilayered composite films for tactile sensors is a novel strategy to achieve high-performance tactile perception in real-time health monitoring and 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.

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

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

Multimodal-Synergistic-Modulation Neuromorphic Imaging Systems for Simulating Dry Eye Imaging

Inspired by human eyes, the neuromorphic visual system employs a highly efficient imaging and recognition process, which offers tremendous advantages in image acquisition, data pre-processing, and dynamic storage. However, it is still an enormous challenge to simultaneously simulate the structure, function, and environmental adaptive behavior of the human eye based on one device. Here, a multimodal-synergistic-modulation neuromorphic imaging system based on ultraflexible synaptic transistors is successfully presented and firstly simulates the dry eye imaging behavior at the device level. Moreover, important functions of the human visual system in relation to optoelectronic synaptic plasticity, image erasure and enhancement, real-time preprocessing, and dynamic storage are simulated by versatile devices. This work not only simplifies the complexity of traditional neuromorphic visual systems, but also plays a positive role in the publicity of biomedical eye care.

Highly Stretchable Graphene Scrolls Transistors for Self-Powered Tribotronic Non-Mechanosensation Application

Owing to highly desired requirements in advanced disease diagnosis, therapy, and health monitoring, noncontact mechanosensation active matrix has drawn considerable attention. To satisfy the practical demands of high energy efficiency, in this report, combining the advantage of multiparameter monitoring, high sensitivity, and high resolution of active matrix field-effect transistor (FET) with triboelectric nanogenerators (TENG), we successfully developed the tribotronic mechanosensation active matrix based on tribotronic ion gel graphene scrolls field-effect transistors (GSFET). The tribopotential produced by TENG served as a gate voltage to modulate carrier transport along the semiconductor channel and realized self-powered ability with considerable decreased energy consumption. To achieve high spatial utilization and more pronounced responsivity of the dielectric of this transistor, ion gel was used to act as a triboelectric layer to conduct friction and contact electrification with external materials directly to produce triboelectric charges to power GFET. This tribopotential-driving device has excellent tactile sensing properties with high sensitivity (1.125 mm^-1), rapid response time (~16 ms), and a durability operation of thousands of cycles. Furthermore, the device was transparent and flexible with the capability of spatially mapping touch stimuli and monitoring real-time temperature. Due to all these unique characteristics, this novel noncontact mechanosensation GSFET active matrix provided a new method for self-powered E-skin with promising potential for self-powered wearable devices and intelligent robots.

Recent Progress in Self-Powered Wireless Sensors and Systems Based on TENG

With the development of 5G, artificial intelligence, and the Internet of Things, diversified sensors (such as the signal acquisition module) have become more and more important in people's daily life. According to the extensive use of various distributed wireless sensors, powering them has become a big problem. Among all the powering methods, the self-powered sensor system based on triboelectric nanogenerators (TENGs) has shown its superiority. This review focuses on four major application areas of wireless sensors based on TENG, including environmental monitoring, human monitoring, industrial production, and daily life. The perspectives and outlook of the future development of self-powered wireless sensors are discussed.

Artificial optical synaptic devices with ultra-low power consumption

A BP/CdS heterostructure-based artificial photonic synapse with an ultra-low power consumption is proposed, presenting great potential in high-performance neuromorphic vision systems.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.

Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing

Devices with sensing-memory-computing capability for the detection, recognition and memorization of real time sensory information could simplify data conversion, transmission, storage, and operations between different blocks in conventional chips, which are invaluable and sought-after to offer critical benefits of accomplishing diverse functions, simple design, and efficient computing simultaneously in the internet of things (IOT) era. Here, we develop a self-powered vertical tribo-transistor (VTT) based on MXenes for multi-sensing-memory-computing function and multi-task emotion recognition, which integrates triboelectric nanogenerator (TENG) and transistor in a single device with the simple configuration of vertical organic field effect transistor (VOFET). The tribo-potential is found to be able to tune ionic migration in insulating layer and Schottky barrier height at the MXene/semiconductor interface, and thus modulate the conductive channel between MXene and drain electrode. Meanwhile, the sensing sensitivity can be significantly improved by 711 times over the single TENG device, and the VTT exhibits excellent multi-sensing-memory-computing function. Importantly, based on this function, the multi-sensing integration and multi-model emotion recognition are constructed, which improves the emotion recognition accuracy up to 94.05% with reliability. This simple structure and self-powered VTT device exhibits high sensitivity, high efficiency and high accuracy, which provides application prospects in future human-mechanical interaction, IOT and high-level intelligence.

Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things

In the era of 5G and the Internet of things (IoTs), various human-computer interaction systems based on the integration of triboelectric nanogenerators (TENGs) and IoTs technologies demonstrate the feasibility of sustainable and self-powered functional systems. The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing, which have greatly changed the way of human production and daily life. This review mainly introduced the TENG applications in multidiscipline scenarios of IoTs, including smart agriculture, smart industry, smart city, emergency monitoring, and machine learning-assisted artificial intelligence applications. The challenges and future research directions of TENG toward IoTs have also been proposed. The extensive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.

Robust 2D MoS2 Artificial Synapse Device Based on a Lithium Silicate Solid Electrolyte for High-Precision Analogue Neuromorphic Computing

High-precision artificial synaptic devices compatible with existing CMOS technology are essential for realizing robust neuromorphic hardware systems with reliable parallel analogue computation beyond the von Neumann serial digital computing architecture. However, critical issues related to reliability and variability, such as nonlinearity and asymmetric weight updates, have been great challenges in the implementation of artificial synaptic devices in practical neuromorphic hardware systems. Herein, a robust three-terminal two-dimensional (2D) MoS₂ artificial synaptic device combined with a lithium silicate (LSO) solid-state electrolyte thin film is proposed. The rationally designed synaptic device exhibits excellent linearity and symmetry upon electrical potentiation and depression, benefiting from the reversible intercalation of Li ions into the MoS₂ channel. In particular, extremely low cycle-to-cycle variations (3.01%) during long-term potentiation and depression processes over 500 pulses are achieved, causing statistical analogue discrete states. Thus, a high classification accuracy of 96.77% (close to the software baseline of 98%) is demonstrated in the Modified National Institute of Standards and Technology (MNIST) simulations. These results provide a future perspective for robust synaptic device architecture of lithium solid-state electrolytes stacked with 2D van der Waals layered channels for high-precision analogue neuromorphic computing systems.

Optical synaptic devices with ultra-low power consumption for neuromorphic computing

Brain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system. In this study, we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 108 A W-1 at VD = 0.5 V and light power intensity of 0.16 μW cm-2 (1.78 × 108 A W-1 on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process, representing the lowest among the reported results. Finally, a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.

Bioinspired Artificial Motion Sensory System for Rotation Recognition and Rapid Self-Protection

As one of the most common synergies between the exteroceptors and proprioceptors, the synergy between visual and vestibule enables the human brain to judge the state of human motion, which is essential for motion recognition and human self-protection. Hence, in this work, an artificial motion sensory system (AMSS) based on artificial vestibule and visual is developed, which consists of a tribo-nanogenerator (TENG) as a vestibule that can sense rotation and synaptic transistor array as retina. The principle of temporal congruency has been successfully realized by multisensory input. In addition, pattern recognition results show that the accuracy of multisensory integration is more than 15% higher than that of single sensory. Moreover, due to the rotation recognition and visual recognition functions of AMSS, we realized multimodal information recognition including angles and numbers in the spiking correlated neural network (SCNN), and the accuracy rate reached 89.82%. Besides, the rapid self-protection of a human was successfully realized by AMSS in the case of simulated amusement rides, and the reaction time of multiple motion sensory integration is only one-third of that of a single vestibule. The development of AMSS based on the synergy of simulated vision and vestibule will show great potential in neural robot, artificial limbs, and soft electronics.

Triboelectric Potential Powered High-Performance Organic Transistor Array

Triboelectric potential gated transistors have inspired various applications toward mechanical behavior controlled logic circuits, multifunctional sensors, artificial sensory neurons, etc. Their rapid development urgently calls for high-performance devices and corresponding figure of merits to standardize the tribotronic gating properties. Organic semiconductors paired with solution processability promise low-cost manufacture of high-performance tribotronic transistor devices/arrays. Here, we demonstrate a record high-performance tribotronic transistor array composed of an integrated triboelectric nanogenerator (TENG) and a large-area device array of C8-BTBT-PS transistors. The working mechanism of effective triboelectric potential gating is elaborately explained from the aspect of conjugated energy bands of the contact-electrification mediums and organic semiconductors. Driven by the triboelectric potential, the tribotronic transistor shows superior properties of record high current on/off ratios (>10⁸), a steep subthreshold swing (29.89 μm/dec), high stability, and excellent reproducibility. Moreover, tribotronic logic devices modulated by mechanical displacement have also been demonstrated with good stability and a high gain of 1260 V/mm. The demonstrated large-area tribotronic transistor array of organic semiconductor exhibits record high performance and offers an effective R&D platform for mechano-driven electronic terminals, interactive intelligent system, artificial robotic skin, etc.

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.

An artificial remote tactile device with 3D depth-of-field sensation

Flexible tactile neuromorphic devices are becoming important as the impetus for the development of human-machine collaboration. However, accomplishing and further transcending human intelligence with artificial intelligence still confront many barriers. Here, we present a self-powered stretchable three-dimensional remote tactile device (3D-RTD) that performs the depth-of-field (DOF) sensation of external mechanical motions through a conductive-dielectric heterogeneous structure. The device can build a logic relationship precisely between DOF motions of an external active object and sensory potential signals of bipolar sign, frequency, amplitude, etc. The sensory mechanism is revealed on the basis of the electrostatic theory and multiphysics modeling, and the performance is verified via an artificial-biological hybrid system with micro/macroscale interaction. The feasibility of the 3D-RTD as an obstacle-avoidance patch for the blind is systematically demonstrated with a rat. This work paves the way for multimodal neuromorphic device that transcends the function of a biological one toward a new modality for brain-like intelligence.

Skin-Inspired Thermoreceptors-Based Electronic Skin for Biomimicking Thermal Pain Reflexes

Electronic systems possessing skin-like morphology and functionalities (electronic skins [e-skins]) have attracted considerable attention in recent years to provide sensory or haptic feedback in growing areas such as robotics, prosthetics, and interactive systems. However, the main focus thus far has been on the distributed pressure or force sensors. Herein a thermoreceptive e-skin with biological systems like functionality is presented. The soft, distributed, and highly sensitive miniaturized (≈700 µm2 ) artificial thermoreceptors (ATRs) in the e-skin are developed using an innovative fabrication route that involves dielectrophoretic assembly of oriented vanadium pentoxide nanowires at defined locations and high-resolution electrohydrodynamic printing. Inspired from the skin morphology, the ATRs are embedded in a thermally insulating soft nanosilica/epoxy polymeric layer and yet they exhibit excellent thermal sensitivity (-1.1 ± 0.3% °C-1 ), fast response (≈1s), exceptional stability (negligible hysteresis for >5 h operation), and mechanical durability (up to 10 000 bending and twisting loading cycles). Finally, the developed e-skin is integrated on the fingertip of a robotic hand and a biological system like reflex is demonstrated in response to temperature stimuli via localized learning at the hardware level.

Emerging Iontronic Sensing: Materials, Mechanisms, and Applications

Iontronic sensors represent a novel class of soft electronics which not only replicate the biomimetic structures and perception functions of human skin but also simulate the mechanical sensing mechanism. Relying on the similar mechanism with skin perception, the iontronic sensors can achieve ion migration/redistribution in response to external stimuli, promising iontronic sensing to establish more intelligent sensing interface for human-robotic interaction. Here, a comprehensive review on advanced technologies and diversified applications for the exploitation of iontronic sensors toward ionic skins and artificial intelligence is provided. By virtue of the excellent stretchability, high transparency, ultrahigh sensitivity, and mechanical conformality, numerous attempts have been made to explore various novel ionic materials to fabricate iontronic sensors with skin-like perceptive properties, such as self-healing and multimodal sensing. Moreover, to achieve multifunctional artificial skins and intelligent devices, various mechanisms based on iontronics have been investigated to satisfy multiple functions and human interactive experiences. Benefiting from the unique material property, diverse sensing mechanisms, and elaborate device structure, iontronic sensors have demonstrated a variety of applications toward ionic skins and artificial intelligence.

Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing

Biological nervous systems evolved in nature have marvelous information processing capacities, which have great reference value for modern information technologies. To expand the function of electronic devices with applications in smart health monitoring and treatment, wearable energy-efficient computing, neuroprosthetics, etc., flexible artificial synapses for neuromorphic computing will play a crucial role. Here, carbon nanotube-based ferroelectric synaptic transistors are realized on ultrathin flexible substrates via a low-temperature approach not exceeding 90 °C to grow ferroelectric dielectrics in which the single-pulse, paired-pulse, and repetitive-pulse responses testify to well-mimicked plasticity in artificial synapses. The long-term potentiation and long-term depression processes in the device demonstrate a dynamic range as large as 2000×, and 360 distinguishable conductance states are achieved with a weight increase/decrease nonlinearity of no more than 1 by applying stepped identical pulses. The stability of the device is verified by the almost unchanged performance after the device is kept in ambient conditions without additional passivation for 240 days. An artificial neural network-based simulation is conducted to benchmark the hardware performance of the neuromorphic devices in which a pattern recognition accuracy of 95.24% is achieved.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing

Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous studies are mostly focused on the emulation of the basic memory functions of homo-synapses using single-gate transistors, multi-gate transistors offer opportunities for the mimicry of more complex and advanced memory formation behaviors in biological hetero-synapses. In this work, we demonstrate an artificial hetero-synapse based on a dual-gate electrolyte transistor that can implement in situ spatiotemporal information integration and storage. We show that electric pulses applied on a single gate or unsynchronized electric pulses applied on dual gates only induce volatile conductance modulation for short-term memory emulation. In contrast, the device integrates the electric pulses coincidently applied on the dual gates in a supralinear manner and exhibits nonvolatile conductance modulation, enabling long-term memory emulation. Further studies prove that artificial neural networks based on such hetero-synaptic transistors can autonomously filter the random noise signals in the dual-gate inputs during spatiotemporal integration, facilitating the formation of accurate and stable memory. Compared to the single-gate synaptic transistor, the classification accuracy of MNIST handwritten digits using the hetero-synaptic transistor is improved from 89.3% to 99.0%. These findings demonstrate the great potential of multi-gate hetero-synaptic transistors in simulating complex spatiotemporal information processing functions and provide new platforms for the design of advanced neuromorphic computing systems.

Tactile tribotronic reconfigurable p-n junctions for artificial synapses

The emulation of biological synapses with learning and memory functions and versatile plasticity is significantly promising for neuromorphic computing systems. Here, a robust and continuously adjustable mechanoplastic semifloating-gate transistor is demonstrated based on an integrated graphene/hexagonal boron nitride/tungsten diselenide van der Waals heterostructure and a triboelectric nanogenerator (TENG). The working states (p-n junction or n⁺-n junction) can be manipulated and switched under the sophisticated modulation of triboelectric potential derived from mechanical actions, which is attributed to carriers trapping and detrapping in the graphene layer. Furthermore, a reconfigurable artificial synapse is constructed based on such mechanoplastic transistor that can simulate typical synaptic plasticity and implement dynamic control correlations in each response mode by further designing the amplitude and duration. The artificial synapse can work with ultra-low energy consumption at 74.2 fJ per synaptic event and the extended synaptic weights. Under the synergetic effect of the semifloating gate, the synaptic device can enable successive mechanical facilitation/depression, short-/long-term plasticity and learning-experience behavior, exhibiting the mechanical behavior derived synaptic plasticity. Such reconfigurable and mechanoplastic features provide an insight into the applications of energy-efficient and real-time interactive neuromodulation in the future artificial intelligent system beyond von Neumann architecture.

An Artificial Reflex Arc That Perceives Afferent Visual and Tactile Information and Controls Efferent Muscular Actions

Neural perception and action-inspired electronics is becoming important for interactive human-machine interfaces and intelligent robots. A system that implements neuromorphic environmental information coding, synaptic signal processing, and motion control is desired. We report a neuroinspired artificial reflex arc that possesses visual and somatosensory dual afferent nerve paths and an efferent nerve path to control artificial muscles. A self-powered photoelectric synapse between the afferent and efferent nerves was used as the key information processor. The artificial reflex arc successfully responds to external visual and tactile information and controls the actions of artificial muscle in response to these external stimuli and thus emulates reflex activities through a full reflex arc. The visual and somatosensory information is encoded as impulse spikes, the frequency of which exhibited a sublinear dependence on the obstacle proximity or pressure stimuli. The artificial reflex arc suggests a promising strategy toward developing soft neurorobotic systems and prostheses.

Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System

A self-powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self-powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low-pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software-based simulation. Furthermore, a fully hardware-based breath-monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.

Rehabilitation of Total Knee Arthroplasty by Integrating Conjoint Isometric Myodynamia and Real-Time Rotation Sensing System

As the world population structure has already exhibited an inevitable trend of aging, technical advances that can provide better eldercare are highly desired. Knee osteoarthritis, one of the most common age-associated diseases, can be effectively treated via total knee arthroplasty (TKA). However, patients are suffering from the recovery process due to inconvenience in post-hospital treatment. Here, a portable, modular, and wearable brace for self-assessment of TKA patients' rehabilitation is reported. This system mainly consists of a force transducer for isometric muscle strength measurement and an active angle sensor for knee bending detection. Clinical experiments on TKA patients demonstrate the feasibility and significance of the system. Specifically, via brace-based personalized healthcare, the TKA patients' rehabilitation process is quantified in terms of myodynamia, and a definite rehabilitation enhancement is obtained. Additionally, new indicators, that is, isometric muscle test score, for evaluating TKA rehabilitation are proposed. It is anticipated that, as the cloud database is employed and more rehabilitation data are collected in the near future, the brace system can not only facilitate rehabilitations of TKA patients, but also improve life quality for geriatric patients and open a new space for remote artificial intelligence medical engineering.

Triboelectric Nanogenerators as Active Tactile Stimulators for Multifunctional Sensing and Artificial Synapses

The wearable tactile sensors have attracted great attention in the fields of intelligent robots, healthcare monitors and human-machine interactions. To create active tactile sensors that can directly generate electrical signals in response to stimuli from the surrounding environment is of great significance. Triboelectric nanogenerators (TENGs) have the advantages of high sensitivity, fast response speed and low cost that can convert any type of mechanical motion in the surrounding environment into electrical signals, which provides an effective strategy to design the self-powered active tactile sensors. Here, an overview of the development in TENGs as tactile stimulators for multifunctional sensing and artificial synapses is systematically introduced. Firstly, the applications of TENGs as tactile stimulators in pressure, temperature, proximity sensing, and object recognition are introduced in detail. Then, the research progress of TENGs as tactile stimulators for artificial synapses is emphatically introduced, which is mainly reflected in the electrolyte-gate synaptic transistors, optoelectronic synaptic transistors, floating-gate synaptic transistors, reduced graphene oxides-based artificial synapse, and integrated circuit-based artificial synapse and nervous systems. Finally, the challenges of TENGs as tactile stimulators for multifunctional sensing and artificial synapses in practical applications are summarized, and the future development prospects are expected.