Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Flexible iontronic sensing

The emerging flexible iontronic sensing (FITS) technology has introduced a novel modality for tactile perception, mimicking the topological structure of human skin while providing a viable strategy for seamless integration with biological systems. With research progress, FITS has evolved from focusing on performance optimization and structural enhancement to a new phase of integration and intelligence, positioning it as a promising candidate for next-generation wearable devices. Therefore, a review from the perspective of technological development trends is essential to fully understand the current state and future potential of FITS devices. In this review, we examine the latest advancements in FITS. We begin by examining the sensing mechanisms of FITS, summarizing research progress in material selection, structural design, and the fabrication of active and electrode layers, while also analysing the challenges and bottlenecks faced by different segments in this field. Next, integrated systems based on FITS devices are reviewed, highlighting their applications in human-machine interaction, healthcare, and environmental monitoring. Additionally, the integration of artificial intelligence into FITS is explored, focusing on optimizing front-end device design and improving the processing and utilization of back-end data. Finally, building on existing research, future challenges for FITS devices are identified and potential solutions are proposed.

All-Optically Modulated In-Sensor Computing Device Based on Ionic-Conducting CuInP2Se6

Inspired by the human visual system, in-sensor computing has emerged as a promising approach to address growing demands for real-time image processing while overcoming constraints in computational resources. However, existing in-sensor computing optoelectronic devices still face challenges such as complex heterostructures or limited optical modulation for operational efficiency, restricting their practical use. Here, a simple two-terminal optoelectronic device has been fabricated using the 2D material CuInP2Se6, achieving neuromorphic functionalities through all-optical modulation. The device exhibits a tunable photoresponse across the visible spectrum (400 to 700 nm) and enables bidirectional conductance modulation in response to light stimuli, driven by the interaction between Cu⁺ ions and photogenerated electrons. It shows high linearity with 300 discrete conductance states under red, green, and blue light, enabling color-specific image feature extraction, processing, and recognition across three channels. This approach significantly enhances color image recognition accuracy by 4.6% when integrated with a three-channel convolutional neural network. Additionally, the bidirectional photoresponse allows for efficient noise suppression during color image preprocessing, leading to a 490% improvement in signal-to-noise ratio. These findings highlight the potential of CuInP2Se6-based architecture for robust performance, paving the way for in-sensor neuromorphic vision systems in artificial intelligence and biomimetic computing.

Ion-Electron Interactions in 2D Nanomaterials-Based Artificial Synapses for Neuromorphic Applications

With the increasing limitations of conventional computing techniques, particularly the von Neumann bottleneck, the brain's seamless integration of memory and processing through synapses offers a valuable model for technological innovation. Inspired by biological synapse facilitating adaptive, low-power computation by modulating signal transmission via ionic conduction, iontronic synaptic devices have emerged as one of the most promising candidates for neuromorphic computing. Meanwhile, the atomic-scale thickness and tunable electronic properties of van der Waals two-dimensional (2D) materials enable the possibility of designing highly integrated, energy-efficient devices that closely replicate synaptic plasticity. This review comprehensively analyzes advancements in iontronic synaptic devices based on 2D materials, focusing on electron-ion interactions in both iontronic transistors and memristors. The challenges of material stability, scalability, and device integration are evaluated, along with potential solutions and future research directions. By highlighting these developments, this review offers insights into the potential of 2D materials in advancing neuromorphic systems.

Enhanced Mechanical Properties of Polyurethane Ionogels by Synergistic Interaction of Phase-Locking Ion and Hydrogen Bonds

Ionogels are widely employed in various ionotronic applications. However, the advancement of ionogels with excellent mechanical properties presents significant challenges due to the interactions between ions and the strong polar sites in polymers, which hinder the interchain/intrachain hydrogen bonding interaction. In this work, ionic liquid (IL) with ureido is dispersed into polyurethane cationomer (PUC) solid network by swelling strategy to prepare PUC ionogels (PUCD/IL). The synergistic effects of phase-locked ions and hydrogen bonding between PUC chain and IL enhance the mechanical properties of the PUCD/IL, allowing the strength and elongation at break of PUCD/IL achieved ≈10.50 MPa and ≈1638.74%, which are enhanced by ≈162.50% and ≈80.74%, respectively, compared with the equivalent pure solvent-treated PUC (PUCD). Furthermore, the strength and elongation at break of the PUC ionogel prepared by the traditional solution blending method (PUCB/IL) are only 6.76% (0.71 MPa) and 25.87% (423.93%) of those of PUCD/IL, respectively. In addition, the PUCD/IL exhibits a wide sensing range (strain: ≈1000%; stress: ≈1000 kPa) and fast response time (104/154 ms), enabling stable and repeatable response to human movement for intelligent human posture monitoring applications. This study provides a strategy for the design and preparation of high-performance PUC ionogels for flexible sensors.

Carbon-based iontronics - current state and future perspectives

Over the past few decades, carbon materials, including fullerenes, carbon nanotubes, graphene, and porous carbons, have achieved tremendous success in the fields of energy, environment, medicine, and beyond, through their development and application. Due to their unique physical and chemical characteristics for enabling simultaneous interaction with ions and transport of electrons, carbon materials have been attracting increasing attention in the emerging field of iontronics in recent years. In this review, we first summarize the recent progress and achievements of carbon-based iontronics (ionic sensors, ionic transistors, ionic diodes, ionic pumps, and ionic actuators) for multiple bioinspired applications ranging from information sensing, processing, and actuation, to simple and basic artificial intelligent reflex arc units for the construction of smart and autonomous iontronics. Additionally, the promising potential of carbon materials for smart iontronics is highlighted and prospects are provided in this review, which provide new insights for the further development of nanostructured carbon materials and carbon-based smart iontronics.

Nanofluidic Memristive Transition and Synaptic Emulation in Atomically Thin Pores

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

Enhanced In-Sensor Computing with Spike Number-Dependent Plasticity Characteristics in an InGaSnO Optical Neuromorphic Device for Accelerating Machine Vision

In-sensor computing systems based on optical neuromorphic devices have attracted increasing attention to improve the efficiency and accuracy of machine vision systems. However, most materials used in optical neuromorphic devices exhibit spike timing-dependent plasticity (STDP) behavior in response to input light signals, leading to complex in-sensor computing and reduced machine vision accuracy. To address this issue, we introduce an indium gallium tin oxide (IGTO) semiconductor designed to enhance spike number-dependent plasticity (SNDP) in response to input light signals while eliminating the STDP behavior. Here, an IGTO-based optical neuromorphic device shows enhanced SNDP characteristics, which are attributed to the strong Sn-O bonding, as verified by photoemission spectroscopy (PES) analysis. The IGTO-based device consistently reaches the same conduction state after 8 light pulses regardless of the pulse timing and also achieves a conduction state based on the number of input light pulses even when 15 different pulse sets are applied. These results exhibit the SNDP characteristics of the IGTO-based device. Notably, in-sensor computing with the SNDP-enhanced device reduces multilayer perceptron (MLP) training time by 87.7% while achieving high classification accuracy. This study demonstrates that in-sensor computing systems with SNDP characteristics in optical neuromorphic devices have significant potential to accelerate machine learning for highly efficient machine vision systems.

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

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

Ephaptic Coupling in Ultralow-Power Ion-Gel Nanofiber Artificial Synapses for Enhanced Working Memory

Neuromorphic devices are designed to replicate the energy-efficient information processing advantages found in biological neural networks by emulating the working mechanisms of neurons and synapses. However, most existing neuromorphic devices focus primarily on functionally mimicking biological synapses, with insufficient emphasis on ion transport mechanisms. This limitation makes it challenging to achieve the complexity and connectivity inherent in biological systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based on a flexible ion-gel nanofiber network is proposed, which transmits information and enables ephaptic coupling through capacitance formation by ion transport with an extremely low energy consumption of just 6 femtojoules. The hysteretic ion transport behavior endows the device with synaptic-like memory effects, significantly enhancing the performance of the reservoir computing system for classifying the MNIST handwritten digit dataset and demonstrating high efficiency in edge learning. More importantly, the devices in an array establish communication connections, exhibiting global oscillatory behaviors similar to ephaptic coupling in biological neural networks. This connectivity enables the array to perform working memory tasks, paving the way for developing brain-like systems characterized by high complexity and vast connectivity.

Emerging Liquid-Based Memristive Devices for Neuromorphic Computation

To mimic the neural functions of the human brain, developing hardware with natural similarities to the human nervous system is crucial for realizing neuromorphic computing architectures. Owing to their capability to emulate artificial neurons and synapses, memristors are widely regarded as a leading candidate for achieving neuromorphic computing. However, most current memristor devices are solid-state. In contrast, biological nervous systems operate within an aqueous environment, and the human brain accomplishes intelligent behaviors such as information generation, transmission, and memory by regulating ion transport in neuronal cells. To achieve computing systems that are more analogous to biological systems and more energy-efficient, memristor devices based on liquid environments are developed. In contrast to traditional solid-state memristors, liquid-based memristors possess advantages such as anti-interference, low energy consumption, and low heat generation. Simultaneously, they demonstrate excellent biocompatibility, rendering them an ideal option for the next generation of artificial intelligence systems. Numerous experimental demonstrations of liquid-based memristors are reported, showcasing their unique memristive properties and novel neuromorphic functionalities. This review focuses on the recent developments in liquid-based memristors, discussing their operating mechanisms, structures, and functional characteristics. Additionally, the potential applications and development directions of liquid-based memristors in neuromorphic computing systems are proposed.

Flexible Neuromorphic Electronics for Wearable Near-Sensor and In-Sensor Computing Systems

Flexible neuromorphic architectures that emulate biological cognitive systems hold great promise for smart wearable electronics. To realize neuro-inspired sensing and computing electronics, artificial sensory neurons that detect and process external stimuli must be integrated with central nervous systems capable of parallel computation. In near-sensor computing, synaptic devices, and sensors are used to emulate sensory neurons and receptors, respectively. In contrast, in in-sensor computing, a single multifunctional device serves as both the receptor and neuron. Bio-inspired cognitive systems efficiently detect and process stimuli through data structuring techniques, significantly reducing data volume and enabling the extension of neuromorphic applications to smart wearable systems. To construct wearable near- and in-sensor computing, it is crucial to develop artificial sensory neurons and central nervous synapses that replicate the biological functionalities. Additionally, the integrated systems must exhibit high mechanical flexibility and integration density. This review addresses research on flexible bio-inspired cognitive systems, classified into near- and in-sensor computing. It covers fundamental aspects, including biological cognitive processes, the required components, and the structures for each component, as well as applications for wearable smart systems. Finally, it offers perspectives on future research directions for flexible neuromorphic electronics in smart wearable systems connected to the next-generation Internet of Things.

Biomaterials for neuroengineering: applications and challenges

Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.

An optoelectrochemical synapse based on a single-component n-type mixed conductor

Organic mixed ionic-electronic conductors (OMIECs) are materials that can be used to build bio-inspired electronic devices as they emulate ion-based cellular communication through doping with aqueous ionic charges. The integration of charge photogeneration and electrochemical doping processes in the polymer film enables optoelectronic applications that involve synaptic transistors. However, no OMIEC has yet been implemented to create a miniaturized photoactive platform capable of perceiving and processing multi-spectral visual information. Here, we present a materials and device design concept in which an n-type OMIEC film is incorporated into the micron-scale channel of an electrochemical transistor operating directly in an aqueous electrolyte under ambient conditions. The conjugated polymer channel, consisting of a fluorinated bisistain-lactone-bithiazole acceptor, has a current modulated in response to both electrical and optical stimuli, emulating the multimodal function of the visual nervous system. Our optoelectrochemical synapse achieves multilevel conductance states as well as transduction of visual information covering ultraviolet, visible, and near-infrared regions of the spectrum - a range beyond that of the human visual system's perception. The resulting transistor active-matrix array is capable of adaptive sensing, memory, and pre-processing of visual information, demonstrating an efficient optoelectronic neuromorphic system with multi-task learning capability.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

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

Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal-oxide-semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

Bioactive Ion-Confined Ultracapacitive Memristors with Neuromorphic Functions

The field of bioinspired iontronics, bridging electronic devices and ionic systems, has multiple biological applications. Carbon-based ultracapacitive devices hold promise for controlling bioactive ions via electric double layers due to their high-surface-area and biocompatible porous carbon electrodes. However, the interplay between complex bioactive ions and porous carbons remains unclear due to the variety of structures of bioactive ions present in biological systems. Herein, we investigate the adsorption behavior of a series of bioactive ammonium-based cations with varying alkyl chain lengths in nanoporous carbons. We find that strong physisorption results from the synergistic hydrophobic interaction and electrostatic attraction between porous carbons (with a negative zeta potential) and bioactive cations. Bioactive cations with varying alkyl chain lengths can be irreversibly physically adsorbed and confined within nanoporous carbons resulting in anion enrichment and depletion during electric polarization. This situation, in turn, results in a characteristic memristive behavior in all-carbon capacitive ionic memristor devices. Our findings highlight the relationship between the resistance state of the memristor and ion adsorption mechanisms in all-carbon capacitive devices, which hold potential for future transmitter delivery, biointerfacing, and neuromorphic devices.

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.

Wearable and implantable biosensors: mechanisms and applications in closed-loop therapeutic systems

This review article examines the current state of wearable and implantable biosensors, offering an overview of their biosensing mechanisms and applications. We also delve into integrating these biosensors with therapeutic systems, discussing their operational principles and incorporation into closed-loop devices. Biosensing strategies are broadly categorized into chemical sensing for biomarker detection, physical sensing for monitoring physiological conditions such as pressure and temperature, and electrophysiological sensing for capturing bioelectrical activities. The discussion extends to recent developments in drug delivery and electrical stimulation devices to highlight their significant role in closed-loop therapy. By integrating with therapeutic devices, biosensors enable the modulation of treatment regimens based on real-time physiological data. This capability enhances the patient-specificity of medical interventions, an essential aspect of personalized healthcare. Recent innovations in integrating biosensors and therapeutic devices have led to the introduction of closed-loop wearable and implantable systems capable of achieving previously unattainable therapeutic outcomes. These technologies represent a significant leap towards dynamic, adaptive therapies that respond in real-time to patients' physiological states, offering a level of accuracy and effectiveness that is particularly beneficial for managing chronic conditions. This review also addresses the challenges associated with biosensor technologies. We also explore the prospects of these technologies to address their potential to transform disease management with more targeted and personalized treatment solutions.

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

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

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.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

Gate-All-Around Nanopore Osmotic Power Generators

Nanofluidic channels in a membrane represent a promising avenue for harnessing blue energy from salinity gradients, relying on permselectivity as a pivotal characteristic crucial for inducing electricity through diffusive ion transport. Surface charge emerges as a central player in the osmotic energy conversion process, emphasizing the critical significance of a judicious selection of membrane materials to achieve optimal ion permeability and selectivity within specific channel dimensions. Alternatively, here we report a field-effect approach for in situ manipulation of the ion selectivity in a nanopore. Application of voltage to a surround-gate electrode allows precise adjustment of the surface charge density at the pore wall. Leveraging the gating control, we demonstrate permselectivity turnover to enhanced cation selective transport in multipore membranes, resulting in a 6-fold increase in the energy conversion efficiency with a power density of 15 W/m2 under a salinity gradient. These findings not only advance our fundamental understanding of ion transport in nanochannels but also provide a scalable and efficient strategy for nanoporous membrane osmotic power generation.

Low power nanoscale S-FED based single ended sense amplifier applied in integrate and fire neuron circuit

Current advancements in neuromorphic computing systems are focused on decreasing power consumption and enriching computational functions. Correspondingly, state-of-the-art system-on-chip developers are encouraged to design nanoscale devices with minimum power dissipation and high-speed operation. This paper deals with designing a sense amplifier based on side-contacted field-effect diodes to reduce the power-delay product (PDP) and the noise susceptibility, as critical factors in neuron circuits. Our findings reveal that both static and dynamic power consumption of the S-FED-based sense amplifier, equal to 1.86 μW and 1.92 fW/GHz, are × 243.03 and × 332.83 lower than those of the conventional CMOS counterpart, respectively. While the sense-amplifier circuit based on CMOS technology undergoes an output voltage deviation of 170.97 mV, the proposed S-FED-based one enjoys a minor output deviation of 27.31 mV. Meanwhile, the superior HIGH-level and LOW-level noise margins of the S-FED-based sense amplifier to the CMOS counterparts (∆NMH = 70 mV and ∆NML = 120 mV), respectively, can ensure the system-level operation stability of the former one. Subsequent to the attainment of an area-efficient, low-power, and high-speed S-FED-based sense amplifier (PDP = 187.75 × 10-18 W s) as a fundamental building block, devising an innovative integrate-and-fire neuron circuit based on S-FED paves the way to realize a new generation of neuromorphic architectures. To shed light on this context, an S-FED-based integrate-and-fire neuron circuit is designed and analyzed utilizing a sense amplifier and feedback loop to enhance spiking voltage and subsequent noise immunity in addition to an about fourfold increase in firing frequency compared to CMOS-based ones.

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Vacancy-Engineered Nickel Ferrite Forming-Free Low-Voltage Resistive Switches for Neuromorphic Circuits

Innovations in resistive switching devices constitute a core objective for the development of ultralow-power computing devices. Forming-free resistive switching is a type of resistive switching that eliminates the need for an initial high voltage for the formation of conductive filaments and offers promising opportunities to overcome the limitations of traditional resistive switching devices. Here, we demonstrate mixed charge state oxygen vacancy-engineered electroforming-free resistive switching in NiFe2O4 (NFO) thin films, fabricated as asymmetric Ti/NFO/Pt heterostructures, for the first time. Using pulsed laser deposition in a controlled oxygen atmosphere, we tune the oxygen vacancies together with the cationic valence state in the nickel ferrite phase, with the latter directly affecting the charge state of the oxygen vacancies. The structural integrity and chemical composition of the films are confirmed by X-ray diffraction and hard X-ray photoelectron spectroscopy, respectively. Electrical transport studies reveal that resistive switching characteristics in the films can be significantly altered by tuning the amount and charge state of the oxygen vacancy concentration during the deposition of the films. The resistive switching mechanism is seen to depend upon the migration of both singly and doubly charged oxygen vacancies formed as a result of changes in the nickel valence state and the consequent formation/rupture of conducting filaments in the switching layer. This is supported by the existence of an optimum oxygen vacancy concentration for efficient low-voltage resistive switching, below or above which the switching process is inhibited. Along with the filamentary switching mechanism, the Ti top electrode also enhances the resistive switching performance due to interfacial effects. Time-resolved measurements on the devices display both long- and short-term potentiation in the optimized vacancy-engineered NFO resistive switches, ideal for solid-state synapses achieved in a single system. Our work on correlated oxide forming-free resistive switches holds significant potential for CMOS-compatible low-power, nonvolatile resistive memory and neuromorphic circuits.

Anti-Ambipolar Heterojunctions: Materials, Devices, and Circuits

Anti-ambipolar heterojunctions are vital in constructing high-frequency oscillators, fast switches, and multivalued logic (MVL) devices, which hold promising potential for next-generation integrated circuit chips and telecommunication technologies. Thanks to the strategic material design and device integration, anti-ambipolar heterojunctions have demonstrated unparalleled device and circuit performance that surpasses other semiconducting material systems. This review aims to provide a comprehensive summary of the achievements in the field of anti-ambipolar heterojunctions. First, the fundamental operating mechanisms of anti-ambipolar devices are discussed. After that, potential materials used in anti-ambipolar devices are discussed with particular attention to 2D-based, 1D-based, and organic-based heterojunctions. Next, the primary device applications employing anti-ambipolar heterojunctions, including anti-ambipolar transistors (AATs), photodetectors, frequency doublers, and synaptic devices, are summarized. Furthermore, alongside the advancements in individual devices, the practical integration of these devices at the circuit level, including topics such as MVL circuits, complex logic gates, and spiking neuron circuits, is also discussed. Lastly, the present key challenges and future research directions concerning anti-ambipolar heterojunctions and their applications are also emphasized.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Stretchable and neuromorphic transistors for pain perception and sensitization emulation

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

A Transparent Hydrogel-Ionic Conductor with High Water Retention and Self-Healing Ability

This study presents a transparent and ion-conductive hydrogel with suppressed water loss. The hydrogel comprises agarose polymer doped with sucrose and sodium chloride salt (NaCl-Suc/A hydrogel). Sucrose increases the water retention of the agarose gel, and the Na and Cl ions dissolved in the gel provide ionic conductivity. The NaCl-Suc/A gel shows high retention capability and maintains a 45% water uptake after 4 h of drying at 60 °C without encapsulation at the optimum gel composition. The doped NaCl-Suc/A hydrogel demonstrates improved mechanical properties and ionic conductivity of 1.6 × 10-2 (S/cm) compared to the pristine agarose hydrogel. The self-healing property of the gel restores the electrical continuity when reassembled after cutting. Finally, to demonstrate a potential application of the ion-conductive hydrogel, a transparent and flexible pressure sensor is fabricated using the NaCl-Suc/A hydrogel, and its performance is demonstrated. The results of this study could contribute to solving problems with hydrogel-based devices such as rapid dehydration and poor mechanical properties.

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.