Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

Memristor-based adaptive neuromorphic perception in unstructured environments

Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.

A Suspended, 3D Morphing Sensory System for Robots to Feel and Protect

Artificial sensory systems with synergistic touch and pain perception hold substantial promise for environment interaction and human-robot communication. However, the realization of biological skin-like functional integration of sensors with sensitive touch and pain perception still remains a challenge. Here, a concept is proposed of suspended electronic skins enabling 3D deformation-mechanical contact interactions for achieving synergetic ultrasensitive touch and adjustable pain perception. The suspended sensory system can sensitively capture tiny touch stimuli as low as 0.02 Pa and actively perceive pain response with reliable 5200 cycles via 3D deformation and mechanical contact mechanism, respectively. Based on the touch-pain effect, a visualized feedback demo with miniaturized sensor arrays on artificial fingers is rationally designed to give a pain perception mapping on sharp surfaces. Furthermore, the capability is shown of the suspended electronic skin serving as a safe human-robot communication interface from active and passive view through a feedback control system, demonstrating potential in bionic electronics and intelligent robotics.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

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

Strengthening Multi-Factor Authentication Through Physically Unclonable Functions in PVDF-HFP-Phase-Dependent a-IGZO Thin-Film Transistors

For enhanced security in hardware-based security devices, it is essential to extract various independent characteristics from a single device to generate multiple keys based on specific values. Additionally, the secure destruction of authentication information is crucial for the integrity of the data. Doped amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) using poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) induce a dipole doping effect through a phase-transition process, creating physically unclonable function (PUF) devices for secure user information protection. The PUF security key, generated at VGS = 20 V in a 20 × 10 grid, demonstrates uniformity of 42% and inter-Hamming distance (inter-HD) of 49.79% in the β-phase of PVDF-HFP. However, in the γ-phase, the uniformity drops to 22.5%, and inter-HD decreases to 35.74%, indicating potential security key destruction during the phase transition. To enhance security, a multi-factor authentication (MFA) system is integrated, utilizing five security keys extracted from various TFT parameters. The security keys from turn-on voltage (VON), VGS = 20 V, VGS = 30 V, mobility, and threshold voltage (Vth) exhibit near-ideal uniformities and inter-HDs, with the highest values of 58% and 51.68%, respectively. The dual security system, combining phase transition and MFA, establishes a robust protection mechanism for privacy-sensitive user information.

Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks

Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.

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.

Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin

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

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

An Artificial Olfactory System Based on a Chemi-Memristive Device

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Metal-Oxide Heterojunction Optoelectronic Synapse and Multilevel Memory Devices Enabled by Broad Spectral Photocarrier Modulation

Broad spectral response and high photoelectric conversion efficiency are key milestones for realizing multifunctional, low-power optoelectronic devices such as artificial synapse and reconfigurable memory devices. Nevertheless, the wide bandgap and narrow spectral response of metal-oxide semiconductors are problematic for efficient metal-oxide optoelectronic devices such as photonic synapse and optical memory devices. Here, a simple titania (TiO2 )/indium-gallium-zinc-oxide (IGZO) heterojunction structure is proposed for efficient multifunctional optoelectronic devices, enabling widen spectral response range and high photoresponsivity. By overlaying a TiO2 film on IGZO, the light absorption range extends to red light, along with enhanced photoresponsivity in the full visible light region. By implementing the TiO2 /IGZO heterojunction structure, various synaptic behaviors are successfully emulated such as short-term memory/long-term memory and paired pulse facilitation. Also, the TiO2 /IGZO synaptic transistor exhibits a recognition rate up to 90.3% in recognizing handwritten digit images. Moreover, by regulating the photocarrier dynamics and retention behavior using gate-bias modulation, a reconfigurable multilevel (≥8 states) memory is demonstrated using visible light.

Emerging Memtransistors for Neuromorphic System Applications: A Review

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1-xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.

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.

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices

As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO₂/TaO_x/TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO₂ layer. Moreover, Pt/HfO₂/TaO_x/TaN with a 3 nm thick HfO₂ layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO₂/TaO_x/TaN with Pt/TaO_x/TaN, which has insufficient performance to be used as a nociceptor.

Study of carbon nanotube embedded honey as a resistive switching material

In this paper, natural organic honey embedded with carbon nanotubes (CNTs) was studied as a resistive switching material for biodegradable nonvolatile memory in emerging neuromorphic systems. CNTs were dispersed in a honey-water solution with the concentration of 0.2 wt% CNT and 30 wt% honey. The final honey-CNT-water mixture was spin-coated and dried into a thin film sandwiched in between Cu bottom electrode and Al top electrode to form a honey-CNT based resistive switching memory (RSM). Surface morphology, electrical characteristics and current conduction mechanism were investigated. The results show that although CNTs formed agglomerations in the dried honey-CNT film, both switching speed and the stability in SET and RESET process of honey-CNT RSM were improved. The mechanism of current conduction in CNT is governed by Ohm's law in low-resistance state and the low-voltage range in high-resistance state, but transits to the space charge limited conduction at high voltages approaching the SET voltage.

Poly(N,N-dimethyl)acrylamide-based ion-conductive gel with transparency, self-adhesion and rapid self-healing properties for human motion detection

Flexible strain sensors have been extensively studied for their potential value in monitoring human activity and health. However, it is still challenging to develop multifunctional flexible strain sensors with simultaneously high transparency, strong self-adhesion, fast self-healing and excellent tensile properties. In this study, we used N,N-dimethylacrylamide (DMA) in the imidazolium-based ionic liquid 1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl] imide ([BMIM][Tf₂N]) for "one-step" UV irradiation. A poly(N,N-dimethyl)acrylamide (PDMA) ion-conductive gel was prepared by site polymerization. Based on the good compatibility between PDMA and ionic liquid, the prepared ion-conductive gel has good transparency (∼90%), excellent stretchability (1080%), strong self-adhesion (67.57 kPa), fast self-healing (2 s at room temperature) and great antibacterial activity (∼99% bacterial killing efficiency). Moreover, the strain sensor based on the PDMA ion-conductive gel has good electromechanical performance and can detect different human motions. Based on the simple and easy-to-operate preparation method and the endowed multifunctionality of the PDMA ion-conductive gel, it has broad application prospects in the field of flexible electronic devices.

Artificial Neural Pathway Based on a Memristor Synapse for Optically Mediated Motion Learning

Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors. In our ANP, optical stimulations are detected and converted into electrical signals through a flexible perovskite photoreceptor. The acquired electrical signals are further processed in a zeolitic imidazolate frameworks-8 (ZIF-8)-based memristor device. By controlling the growth of the ZIF-8 nanoparticles, the conductance of the memristor can be finely modulated with electrical stimulations to mimic the modulation of synaptic plasticity. The device is employed in the ANP to implement synaptic functions of learning and memory. Subsequently, the synaptic feedbacks are used to direct a robotic arm to perform responding motions. Upon repeatedly "reviewing" the optical stimulation, the ANP is able to learn, memorize, and complete the specific motions. This work provides a promising strategy toward the design of intelligent autonomous devices and bioinspired robots through memristor-based systems.

Transient Response and Firing Behaviors of Memristive Neuron Circuit

The signal transmission mechanism of the Resistor-Capacitor (RC) circuit is similar to the intracellular and extracellular signal propagating mechanism of the neuron. Thus, the RC circuit can be utilized as the circuit model of the neuron cell membrane. However, resistors are electronic components with the fixed-resistance and have no memory properties. A memristor is a promising neuro-morphological electronic device with nonvolatile, switching, and nonlinear characteristics. First of all, we consider replacing the resistor in the RC neuron circuit with a memristor, which is named the Memristor-Capacitor (MC) circuit, then the MC neuron model is constructed. We compare the charging and discharging processes between the RC and MC neuron circuits. Secondly, two models are compared under the different external stimuli. Finally, the synchronous and asynchronous activities of the RC and MC neuron circuits are performed. Extensive experimental results suggest that the charging and discharging speed of the MC neuron circuit is faster than that of the RC neuron circuit. Given sufficient time and proper external stimuli, the RC and MC neuron circuits can produce the action potentials. The synchronous and asynchronous phenomena in the two neuron circuits reproduce nonlinear dynamic behaviors of the biological neurons.

Printed synaptic transistor-based electronic skin for robots to feel and learn

An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.

Dynamic Instability and Time Domain Response of a Model Halide Perovskite Memristor for Artificial Neurons

Memristors are candidate devices for constructing artificial neurons, synapses, and computational networks for brainlike information processing and sensory-motor autonomous systems. However, the dynamics of natural neurons and synapses are challenging and cannot be well reproduced with standard electronic components. Halide perovskite memristors operate by mixed ionic-electronic properties that may lead to replicate the live computation elements. Here we explore the dynamical behavior of a halide perovskite memristor model to evaluate the response to a step perturbation and the self-sustained oscillations that produce analog neuron spiking. As the system contains a capacitor and a voltage-dependent chemical inductor, it can mimic an action potential in response to a square current pulse. Furthermore, we discover a property that cannot occur in the standard two-dimensional model systems: a three-dimensional model shows a dynamical instability that produces a spiking regime without the need for an intrinsic negative resistance. These results open a new pathway to create spiking neurons without the support of electronic circuits.

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

Organic electrochemical neurons and synapses with ion mediated spiking

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

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.

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Highly Self-Healable Write-Once-Read-Many-Times Devices Based on Polyvinylalcohol-Imidazole Modified Graphene Nanocomposites

Repetitious mechanical stress or external mechanical impact can damage wearable electronic devices, leading to serious degradations in their electrical performances, which limits their applications. Because self-healing would be an excellent solution to the above-mentioned issue, this paper presents a self-healable memory device based on a novel nanocomposite layer consisting of a polyvinyl alcohol matrix and imidazole-modified graphene quantum dots. The device exhibits reliable electrical performance over 600 cycles, and the electrical properties of the device are maintained without any failure under this bending stress. Further, it is confirmed that the damaged device can recover its original electric characteristics after the self-healing process. It is believed that such outstanding results will lead the way to the realization of future wearable electronic systems.

A Bioinspired Stretchable Sensory-Neuromorphic System

Conventional stretchable electronics that adopt a wavy design, a neutral mechanical plane, and conformal contact between abiotic and biotic interfaces have exhibited diverse skin-interfaced applications. Despite such remarkable progress, the evolution of intelligent skin prosthetics is challenged by the absence of the monolithic integration of neuromorphic constituents into individual sensing and actuating components. Herein, a bioinspired stretchable sensory-neuromorphic system, comprising an artificial mechanoreceptor, artificial synapse, and epidermal photonic actuator is demonstrated; these three biomimetic functionalities correspond to a stretchable capacitive pressure sensor, a resistive random-access memory, and a quantum dot light-emitting diode, respectively. This system features a rigid-island structure interconnected with a sinter-free printable conductor, which is optimized by controlling the evaporation rate of solvent (≈160% stretchability and ≈18 550 S cm^-1 conductivity). Devised design improves both areal density and structural reliability while avoiding the thermal degradation of heat-sensitive stretchable electronic components. Moreover, even in the skin deformation range, the system accurately recognizes various patterned stimuli via an artificial neural network with training/inferencing functions. Therefore, the new bioinspired system is expected to be an important step toward implementing intelligent wearable electronics.

Bi-mode electrolyte-gated synaptic transistor via additional ion doping and its application to artificial nociceptors

Multiple types of synaptic transistors that are capable of processing electrical signals similar to the biological neural system hold enormous potential for application in parallel computing, logic circuits and peripheral detection. However, most of these presented synaptic transistors are confined to a single mode of synaptic plasticity under an electrical stimulus, which tremendously limits efficient memory formation and the multifunctional integration of synaptic transistors. Here, we proposed a bi-mode electrolyte-gated synaptic transistor (BEST) with two dynamic processes, the formation of an electrical double layer (EDL) and electrochemical doping (ECD) by tuning the applied voltages, thereby allowing volatile and non-volatile behavior, which is associated with additional ion doping and nanoscale ionic transport. Benefiting from two controllable dynamic processes, we surprisingly found a third state in the transfer curves besides the "off" and "on" states. Moreover, utilizing this unique property, an artificial nociceptor with multilevel modulation of sensitivity was realized based on our bi-mode device. Finally, a haptic sensory system was constructed to exhibit robotic motion that revealed a unique threshold switching behavior, indicating the applicability to peripheral sensing circuits. Hence, the presented bi-mode synaptic transistor provides promising prospects in achieving multiple-mode integrated devices and simplifying neural circuits, which shows great potential in the development of artificial intelligence.

A fully hydrophobic ionogel enables highly efficient wearable underwater sensors and communicators

Underwater sensing has extraordinary significance in ocean exploration (e.g., marine resources development, marine biology research, and marine environment reconnaissance), but the great difference between the marine environment and the land environment seriously prevents current traditional sensors from being applied in underwater sensing. Herein, we reported a fully hydrophobic ionogel with long-term underwater adhesion and stability as a highly efficient wearable underwater sensor that displays an excellent sensing performance, including high sensitivity, rapid responsiveness and superior durability. Of greater significance, the ionogel sensor showed tremendous potential in underwater sensing applications for communication, posture monitoring and marine biological research.

Nanomaterials and their applications on bio-inspired wearable electronics

Wearable electronics featuring conformal attachment, sensitive perception and intellectual signal processing have made significant progress in recent years. However, when compared with living organisms, artificial sensory devices showed undeniable bulky shape, poor adaptability, and large energy consumption. To make up for the deficiencies, biological examples provide inspirations of novel designs and practical applications. In the field of biomimetics, nanomaterials from nanoparticles to layered two-dimensional materials are actively involved due to their outstanding physicochemical properties and nanoscale configurability. This review focuses on nanomaterials related to wearable electronics through bioinspired approaches on three different levels, interfacial packaging, sensory structure, and signal processing, which comprehensively guided recent progress of wearable devices in leveraging both nanomaterial superiorities and biorealistic functionalities. In addition, opinions on potential development trend are proposed aiming at implementing bioinspired electronics in multifunctional portable sensors, health monitoring, and intelligent prosthetics.