Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Advances in memristive gas sensors: A review

With the development of gas sensing technology, traditional semiconductor-based gas sensors are difficult to meet higher performance requirements. To this end, the essence of gas sensor performance improvement depends on the innovation of gas-sensing mechanism. Gas sensors based on the memristor structure (gasistors) have been proposed in recent years, which brings new research ideas for further gas sensors development. Here, we demonstrate a comprehensive overview of the gasistor structures, fabrication, performance, applications and mechanisms. Gasistor structures are compatible with memristors and gas sensors, ranging from typical sandwich structures to those with modified electrodes and porous resistive layers aimed to balance resistive switching and gas sensing functions. Meanwhile, the fabrication process involves common materials such as metals and metal oxides, while novel materials are being explored to optimize performance. It is worth noting that gasistors exhibit unique performance including room temperature sensing, variable gas selectivity, tunable recovery and self-heating against humidity. In applications, apart from gas monitoring, gasistors are used as gas-triggered switches for accident recording, and as olfactory synapses for learning memory. The gas-sensing mechanism is respectively elucidated on the molecular and atomic scales, breaking through the surface conductivity-type mechanism. Finally, the prospects and challenges of gasistors are discussed.

Strain-Insensitive, Air-Stable Stretchable Carbon Nanotube-Based Synaptic Transistors Array via Direct Microfabrication for Neuromorphic Computing

Stretchable synaptic transistors show great promise in mimicking brain activities in soft robotics and skin electronics applications. However, the fabrication of such device arrays on elastic substrates with high stability, throughput, and yield remains challenging. Here, we have developed an approach to fabricate stretchable synaptic transistors directly on elastic substrates, in which carbon nanotubes and SU-8 are used as channel and dielectric, respectively. This method employs a fully photolithography-based microfabrication process that operates at relatively low temperatures. The devices exhibit an average on-off ratio of 2 × 106 and show minimal degradation when stretched up to 40%. Single-pulse, paired-pulse, and repetitive-pulse responses are also demonstrated, showing their ability to work as artificial synapses. The devices exhibit a high linearity of ≤1 with 100 distinct conductance states in long-term plasticity and a dynamic range of 15. Furthermore, we conducted a handwritten digit recognition simulation, achieving a learning accuracy of over 90%. We believe our work can serve as a guide for developing high-performance stretchable synaptic devices for various applications.

Heterointerface-Modulated Synthetic Synapses Exhibiting Complex Multiscale Plasticity

An asymmetric dual-gate heterointerface-regulated artificial synapse (HRAS) is developed, utilizing a main gate with distinct ion concentrations and a lateral gate to receive synaptic pulses, and through dielectric coupling and ionic effects, formed indium tin zinc oxide (ITZO) dual-interface channels that allow precise control over channel charge, thereby simulating multi-level coordinated actions of dual-neurotransmitters. The lateral modulation of the lateral gate significantly regulates ionic effects, achieving the intricate interplay among lateral inhibition/enhancement and short-/long-term plasticity at a multi-level scale for the first time. This interplay enables the HRAS device to simulate frequency-dependent image filtering and spike number-dependent dynamic visual persistence. By combining temporal synaptic inputs with lateral modulation, HRAS harnesses spatiotemporal properties for bio-inspired cryptographic applications, offering a versatile device-level platform for secure information processing. Furthermore, a novel dual-gate input neural network architecture based on HRAS has been proposed, which aids in weight update and demonstrates enhanced recognition capabilities in neural network tasks, highlighting its role in bio-inspired computing.

Optoelectronic synapses realized on large-scale continuous MoSe2 with Te doping induced tunable memory functions

Synaptic devices with integrated sensing-computing-storage functions are emerging as promising technological solutions to break the memory wall in the von Neuman architecture computing system. 2D semiconductors are ideal candidate materials for artificial synapses due to their superior electronic and optoelectronic properties. In this work, we report robust optoelectronic synapses realized on wafer-scale continuous MoSe2 with Te-doping-induced tunable memory functions. A unique defect engineering strategy of substitutional doping of Te has been adopted to induce Se vacancies in chemical vapour deposition grown MoSe2 films. These vacancies introduce defect states as deep trap levels in the band gap, enabling efficient charge trapping and significantly prolonging the decaying time. The presence of Te doping and Se vacancies was confirmed by PL, Raman, and XPS characterization. Ultra-high vacuum stencil lithography technique has been adopted for the fabrication of arrayed optoelectronic devices that exhibit prominent excitatory postsynaptic currents with the paired-pulse facilitation up to 197% under ultraviolet illumination. Therefore, essential synaptic behaviors like the spike-number-, spike-rate-, and spike-intensity-dependent plasticity have been demonstrated, along with the in-sensor computation application of hardware image sharpening capability. This work offers a new method of vacancy engineering in large-scale 2D semiconductors for future application in integrated neuromorphic devices.

A review of synaptic devices based on organic ferroelectric materials

This article reviews advancements in synaptic devices using organic ferroelectric materials, particularly PVDF and its copolymers. As AI and big data progress, traditional computing faces limitations in storage and power consumption, leading to neuromorphic computing's rise. Ferroelectric memristors exhibit excellent controllability and retention, making them ideal for simulating synapses. The article discusses fabrication methods, performance optimization, and challenges such as thermal stability and integration costs. Ultimately, optimized PVDF-based devices could significantly enhance low-power, high-performance computing and drive innovations in AI and the internet of things.

Neuromorphic devices for electronic skin applications

Neuromorphic devices represent an important advancement in technology, drawing inspiration from the intricate and efficient mechanisms of the human brain. This review paper elucidates the diverse landscape of neuromorphic electronic skin (e-skin) technologies while highlighting their numerous applications. Here, neuromorphic devices for e-skin are classified as two types of direct neuromorphic e-skins combining both neuromorphic devices and sensors, and indirect e-skins separating neuromorphic devices and sensors. In direct neuromorphic e-skins, there are developing devices like memristor-based neuromorphic devices with sensors and transistor-based neuromorphic devices with sensors. On the other hand, indirect types are demonstrated as separated neuromorphic and sensor parts systems through the various interfacing structures. It also describes recent neuromorphic developments in artificial neural networks (ANNs), deep neural networks (DNNs), and convolutional neural networks (CNNs), for the real-time interpretation of sensory data. Moreover, it introduces multimodal sensory feedback, soft and flexible e-skins, and more intuitive human-machine interfaces. This review examines various applications, including smart textiles for the development of next-generation wearable bioelectronics, brain-sensing interfaces that enhance tactile perception, and the integration of human-machine interfaces aimed at replicating the biological sensorimotor loop, which can improve health monitoring and biomedical applications. Additionally, the review also highlights the potential of neuromorphic e-skin in human-robot interaction, particularly in the context of continuous prosthetic control and robotics. Through this analysis, the paper provides insights into current advancements, identifies key challenges, and suggests future research directions for optimizing neuromorphic e-skin devices and expanding their practical implementation.

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.

Biocompatible Neuromorphic Device Array Based on Naturally Sourced Mucin for Implantable Bioelectronics

Although the demand for intelligent implantable bioelectronics is steadily increasing, their progress is hindered by the limited availability of materials with sufficient biocompatibility for implantation. Herein, we propose a neuromorphic device with human brain-inspired biomimetic functionality utilizing naturally sourced mucin as the active layer material. The mucin-based neuromorphic memristor (MNM) array successfully mimics key synaptic behaviors uniformly, including a paired-pulse facilitation index of 122.65%, transition from short-term to long-term memory, long-term potentiation, and long-term depression. In addition to the effect of the defect-rich mucin active layer, these behaviors are enhanced by the presence of a MgOx interfacial layer formed at its interface with the Mg top electrode. The cell cytotoxicity test results demonstrate the superior biocompatibility of the MNM array, which shows a relative cell viability of 108.46% after 72 h of cell culture. Moreover, the artificial neural network simulation demonstrates a recognition rate of 89.93% after 125 training epochs, which suggests that naturally sourced materials, including mucin, can be used in implantable bioelectronics for advanced medical healthcare applications.

Organic Artificial Nerves: Neuromorphic Robotics and Bioelectronics

Neuromorphic electronics are inspired by the human brain's compact, energy-efficient nature and its parallel-processing capabilities. Beyond the brain, the entire human nervous system, with its hierarchical structure, efficiently preprocesses complex sensory information to support high-level neural functions such as perception and memory. Emulating these biological processes, artificial nerve electronics have been developed to replicate the energy-efficient preprocessing observed in human nerves. These systems integrate sensors, artificial neurons, artificial synapses, and actuators to mimic sensory and motor functions, surpassing conventional circuits in sensor-integrated electronics. Organic synaptic transistors (OSTs) are key components in constructing artificial nerves, offering tunable synaptic plasticity for complex sensory processing and the mechanical flexibility required for applications in soft robotics and bioelectronics. Compared to traditional sensor-integrated electronics, early implementations of organic artificial nerves (OANs) incorporating OSTs have demonstrated a higher signal-to-noise ratio, lower power consumption, and simpler circuit designs along with on-device processing capabilities and precise control of actuators and biological limbs, driving progress in neuromorphic robotics and bioelectronics. This paper reviews the materials, device engineering, and system integration of the OAN design, highlights recent advancements in neuromorphic robotics and bioelectronics utilizing the OANs, and discusses current challenges and future research directions.

Room-Temperature Organic Spintronic Devices with Wide Range Magnetocurrent Tuning and Multifunctionality via Electro-Optical Compensation Strategy

In spintronics, devices exhibiting large, widely tunable magnetocurrent (MC) values at room temperature are particularly appealing due to their potential in advanced sensing, data storage, and multifunctional technologies. Organic semiconductors (OSCs), with their rich and unique spin-dependent and (opto-)electronic properties, hold significant promise for realizing such devices. However, current organic devices are constrained by limited design strategies, yielding MC values typically confined to tens of percent, thereby restricting their potential for multifunctional applications. Here, this study introduces an electro-optical compensation strategy to modulate MC values, which synergistically integrates and manages the interplays among carrier transport, spin-dependent reactions, and photogenerated carrier dynamics in OSCs-based devices. This approach achieves ultrahigh room-temperature MC values of +13 200% and -10 600% in the designed devices, with continuous and precise tunability over this range-marking a breakthrough in organic spintronic devices. Building on this achievement, by integrating multiple controllable parameters-light, bias, magnetic field, and mechanical flexibility-into a single device, a flexible, room-temperature, multifunctional device is activated, functioning as the high-sensitivity magnetic field sensor, composite field sensor, magnetic current inverter, and magnetically-controlled artificial synaptic, etc. These findings open an avenue for designing high-performance, multifunctional devices with broad implications for future spintronic-related technologies.

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.

Surface Argon Plasma Treatment Enabled Broadband Optoelectronic Synapses Based on Large-Scale Epitaxial GaSe/GaN Heterojunctions

Creating and tuning charge trapping states by introducing atomic-scale defects are crucial for the optoelectronic synapses that parallelize sensing, processing, and memorizing of optical signals in a single device, which is essential for bioinspired neuromorphic computing. Herein, a mild Ar-plasma treatment approach to enable synaptic behavior in 2D semiconductor devices has been proposed and demonstrated in large-scale epitaxial GaSe/GaN heterostructures. The GaSe films were epitaxially grown on a GaN substrate by physical vapor deposition in an ultrahigh vacuum environment, while the devices were fabricated in situ using a shadow mask-assisted electrode deposition technique. A tailored mild Ar-plasma treatment on the GaSe films has been employed to create atomic-scale defects, which provide charge trapping states in the band gap without making morphological damage, as confirmed by the Raman spectra, scanning electron microscopy, and photoluminescence characterizations. Optoelectronic transport measurement under pulsed illumination of varying wavelengths reveals broadband photoresponse and significantly prolonged response time (×103) that give rise to the superior performance of the synaptic devices. This has been proven by the simulation of classic synaptic behaviors of adaptive pain perception and associative learning. Our work provides an efficient approach to facilitate optoelectronic synaptic behaviors in 2D semiconductor devices.

Tough and self-healing waterborne polyurethane elastomers via hydrogen bonds and oxime-carbamate design for wearable flexible strain sensors

Self-healing waterborne polyurethane elastomers, as functional materials, have been widely applied in flexible wearable devices. However, inevitable crack damage during use significantly limits their service life. Achieving an optimal balance between mechanical strength and self-healing ability remains a major challenge in the development of high-performance self-healing polyurethane materials. In this study, a rigid-soft phase-separated structure with multilayered rigid and flexible supramolecular segments was designed using isophorone isocyanate (IPDI), dimethylglyoxime (DMG), and 6-methyl-1,3,5-triazine-2,4-diamine (AGM) as hard segments, and polytetramethylene ether glycol (PTMEG) as soft segments. AGM establishes a multi-hydrogen bonding network with urethane groups, and DMG introduces dynamically reversible oxime-carbamate bonds, enabling the PU elastomers to achieve a self-healing efficiency of up to 95.5%. Benefiting from the gradient rigidity of the polyurethane, the strong hydrogen bond formed by the urea-carbamate bond and the triazine ring, the elastomer exhibited excellent mechanical properties, with a tensile strength of 33.04 MPa, an elongation at break of 954.79%, and a toughness of 90.66 MJ m-3. Moreover, when the composite conductor was used as an electronic skin, it possessed good self-healing properties with electrical response properties.

Molecular Cocrystal Strategy for Retinamorphic Vision with UV-Vis-NIR Perception and Fast Recognition

Neuromorphic vision sensors capable of multispectral perception and efficient recognition are highly desirable for bioretina emulation, but their realization is challenging. Here, we present a cocrystal strategy for preparing an organic nanowire retinamorphic vision sensor with UV-vis-NIR perception and fast recognition. By leveraging molecular-scale donor-acceptor interpenetration and charge-transfer interfaces, the cocrystal nanowire device exhibits ultrawide photoperception ranging from 350 to 1050 nm, fast photoresponse of 150 ms, high specific detectivity of 8.2 × 1012 Jones, and responsivity of 15 A W-1, as well as retina-like photosynaptic plasticity behaviors. Utilizing the sensor nerve and convolutional neural network, the architecture achieves 90% accuracy in recognizing colorful images. The cocrystal design offers an effective method for constructing nanowire photosynases with high performance in artificial visual systems.

DC field-biased multibit/analog artificial synapse featuring an additional degree of freedom for performance tuning

Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO2/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term. The unbiased device is of short-term plasticity, positive biasing increases the remanence of the recorded events and the device gains long-term plasticity at a specific biasing level determined from the device geometry. The adjustability of the biasing field provides an additional degree of freedom allowing performance tuning; the paired-pulse facilitation index of the device is tuned by the biasing level adjustment providing further functional versatility. An appropriately biased segment provides more than 10 synaptic weight levels linearly depending on the number and duration of the stimulating spikes. The relationship with spike magnitude is exponential. The experimentally determined nonlinearity coefficient of the biased device for 50 potentiating spikes is comparable to the best published data. The spike-timing-dependent plasticity determined experimentally for the biased device in its long-term plasticity mode fits the mathematical relationship developed for biological synapses. Fabricated on a titanium metal foil, the produced memristors are sturdy and flexible making them suitable for wearable and implantable intelligent electronics. Our findings are anticipated to raise the potential of forming artificial synapses out of polycrystalline metal oxide thin films.

Soft Artificial Synapse Electronics

Soft electronics, known for their bendable, stretchable, and flexible properties, are revolutionizing fields such as biomedical sensing, consumer electronics, and robotics. A primary challenge in this domain is achieving low power consumption, often hampered by the limitations of the conventional von Neumann architecture. In response, the development of soft artificial synapses (SASs) has gained substantial attention. These synapses seek to replicate the signal transmission properties of biological synapses, offering an innovative solution to this challenge. This review explores the materials and device architectures integral to SAS fabrication, emphasizing flexibility and stability under mechanical deformation. Various architectures, including floating-gate dielectric, ferroelectric-gate dielectric, and electrolyte-gate dielectric, are analyzed for effective weight control in SASs. The utilization of organic and low-dimensional materials is highlighted, showcasing their plasticity and energy-efficient operation. Furthermore, the paper investigates the integration of functionality into SASs, particularly focusing on devices that autonomously sense external stimuli. Functionalized SASs, capable of recognizing optical, mechanical, chemical, olfactory, and auditory cues, demonstrate promising applications in computing and sensing. A detailed examination of photo-functionalized, tactile-functionalized, and chemoreception-functionalized SASs reveals their potential in image recognition, tactile sensing, and chemosensory applications, respectively. This study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications; however, further research is necessary to address scalability, long-time stability, and utilizing functionalized SASs for prosthetics and in vivo applications through clinical adoption. By providing a comprehensive overview, this paper contributes to the understanding of SASs, bridging research gaps and paving the way toward transformative developments in soft electronics, biomimicking and biointegrated synapse devices, and integrated systems.

Two-Dimensional Materials for Brain-Inspired Computing Hardware

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.

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.

An organic electrochemical neuron for a neuromorphic perception system

Human perception systems are highly refined, relying on an adaptive, plastic, and event-driven network of sensory neurons. Drawing inspiration from Nature, neuromorphic perception systems hold tremendous potential for efficient multisensory signal processing in the physical world; however, the development of an efficient artificial neuron with a widely calibratable spiking range and reduced footprint remains challenging. Here, we report an efficient organic electrochemical neuron (OECN) with reduced footprint (<37 mm2) based on high-performance vertical OECT (vOECT) complementary circuitry enabled by an advanced n-type polymer for balanced p-/n-type vOECT performance. The OECN exhibits outstanding neuronal characteristics, capable of producing spikes with a widely calibratable state-of-the art firing frequency range of 0.130 to 147.1 Hz. Leveraging this capability, we develop a neuromorphic perception system that integrates mechanical sensors with the OECN and integrates them with an artificial synapse for tactile perception. The system successfully encodes tactile stimulations into frequency-dependent spikes, which are further converted into postsynaptic responses. This bioinspired design demonstrates significant potential to advance cyborg and neuromorphic systems, providing them with perceptual capabilities.

Large-scale high uniform optoelectronic synapses array for artificial visual neural network

Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm2, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.

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.

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.

A Biodegradable, Stretchable, Healable, and Self-Powered Optoelectronic Synapse Based on Ionic Gelatins for Neuromorphic Vision System

Optoelectronic synapses have gained increasing attentions as a fundamental building block in the development of neuromorphic visual systems. However, it remains a challenge to integrate multiple functions into a single optoelectronic synapse that can be widely applied in wearable artificial intelligence and implantable neuromorphic vision systems. In this study, a stretchable optoelectronic synapse based on biodegradable ionic gelatin heterojunction is successfully developed. This device exhibits self-powered synaptic plasticity behavior with broad spectral response and excellent elastic properties, yet it degrades rapidly upon disposal. After complete cleavage, the device can be fully repaired within 1 min, which is mainly attributed to the non-covalent interactions between different molecular chains. Moreover, the recovery and reprocessing of the ionic gelatins result in optoelectronic properties that are virtually indistinguishable from their original state, showcasing the resilience and durability of ionic gelatins. The combination of biodegradability, stretchability, self-healing, zero-power consumption, ease of large-scale preparation, and low cost makes the work a major step forward in the development of biodegradable and stretchable optoelectronic synapses.

High-Performance Supercapacitive Pressure Sensors via Height-Grading Micro-Domes of Ionic Conductive Elastomer

Soft capacitive sensors present numerous appealing characteristics, including simple structure, low power consumption, and fast response. However, they often suffer from low sensitivity and a limited linear sensing range. Herein, a concept is presented to enhance the sensitivity and linearity of supercapacitive pressure sensors by functionally grading the heights of macrodomes constructed from a highly elastic and ionic conductive elastomer made of poly(vinyl alcohol) and phosphoric acid (PVA/H3PO4). The resultant supercapacitive sensors exhibit a high sensitivity (423.42 kPa-1), wide linear sensing range (0-400 kPa), ultralow limit of detection (0.48 Pa), and high durability (stable signal outputs up to 5000 cycles of loading/unloading). Additionally, the sensors can maintain consistent sensing performance within a temperature range of 25-40 °C. The potential of the sensor in health monitoring is demonstrated through ultrahigh-resolution weight measurement, pulse detection, and respiration monitoring.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

From lab to wearables: Innovations in multifunctional hydrogel chemistry for next-generation bioelectronic devices

Functional hydrogels have emerged as foundational materials in diagnostics, therapy, and wearable devices, owing to their high stretchability, flexibility, sensing, and outstanding biocompatibility. Their significance stems from their resemblance to biological tissue and their exceptional versatility in electrical, mechanical, and biofunctional engineering, positioning themselves as a bridge between living organisms and electronic systems, paving the way for the development of highly compatible, efficient, and stable interfaces. These multifaceted capability revolutionizes the essence of hydrogel-based wearable devices, distinguishing them from conventional biomedical devices in real-world practical applications. In this comprehensive review, we first discuss the fundamental chemistry of hydrogels, elucidating their distinct properties and functionalities. Subsequently, we examine the applications of these bioelectronics within the human body, unveiling their transformative potential in diagnostics, therapy, and human-machine interfaces (HMI) in real wearable bioelectronics. This exploration serves as a scientific compass for researchers navigating the interdisciplinary landscape of chemistry, materials science, and bioelectronics.

IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.

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.

High-performance electrically responsive artificial muscle materials for soft robot actuation

Traditional robotic devices are often bulky and rigid, making it difficult for them to adapt to the soft and complex shapes of the human body. In stark contrast, soft robots, as a burgeoning class of robotic technology, showcase exceptional flexibility and adaptability, positioning them as compelling contenders for a diverse array of applications. High-performance electrically responsive artificial muscle materials (ERAMMs), as key driving components of soft robots, can achieve efficient motion and deformation, as well as more flexible and precise robot control, attracting widespread attention. This paper reviews the latest advancements in high-performance ERAMMs and their applications in the field of soft robot actuation, using ionic polymer-metal composites and dielectric elastomers as typical cases. Firstly, the definition, characteristics, and electro-driven working principles of high-performance ERAMMs are introduced. Then, the material design and synthesis, fabrication processes and optimization, as well as characterization and testing methods of the ERAMMs are summarized. Furthermore, various applications of two typical ERAMMs in the field of soft robot actuation are discussed in detail. Finally, the challenges and future directions in current research are analyzed and anticipated. This review paper aims to provide researchers with a reference for understanding the latest research progress in high-performance ERAMMs and to guide the development and application of soft robots. STATEMENT OF SIGNIFICANCE.

Bio-Inspired Photosensory Artificial Synapse Based on Functionalized Tellurium Multiropes for Neuromorphic Computing

Nanomaterials like graphene and transition metal dichalcogenides are being explored for developing artificial photosensory synapses with low-power optical plasticity and high retention time for practical nervous system implementation. However, few studies are conducted on Tellurium (Te)-based nanomaterials due to their direct and small bandgaps. This paper reports the superior photo-synaptic properties of covalently bonded Tellurium sulfur oxide (TeSOx) and Tellurium selenium oxide (TeSeOx)nanomaterials, which are fabricated by incorporating S and Se atoms on the surface of Te multiropes using vapor deposition. Unlike pure Te multiropes, the TeSOx and TeSeOx multiropes exhibit controllable temporal dynamics under optical stimulation. For example, the TeSOx multirope-based transistor displays a photosensory synaptic response to UV light (λ = 365 nm). Furthermore, the TeSeOx multirope-based transistor exhibits photosensory synaptic responses to UV-vis light (λ = 365, 565, and 660 nm), reliable electrical performance, and a combination of both photodetector and optical artificial synaptic properties with a maximum responsivity of 1500 AW-1 to 365 nm UV light. This result is among the highest reported for Te-heterostructure-based devices, enabling optical artificial synaptic applications with low voltage spikes (1 V) and low light intensity (21 µW cm-2), potentially useful for optical neuromorphic computing.

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Amphiphilic Interface-Mediated Ion Doping for High Performance Organic Electrochemical Transistors with Hydrophobic Polymers

An organic electrochemical transistor (OECT) is one of the promising devices for bioelectronics due to its high transconductance, encompassing low operation voltage, and good compatibility with aqueous conditions. Despite these advantages, the challenge of balancing ion penetration and electron transport remains a significant issue in OECTs. Herein, we present an amphiphilic interface modification strategy to successfully prepare OECTs in aqueous conditions based on a high-mobility hydrophobic polypyrrole derivative. An amphiphilic interface mixed with an amphiphilic polymer and the active layer markedly promotes ion penetration and results in a significant improvement in performance, with the switch time reduced from several seconds to nearly 100 ms and the transconductance increased by an order of magnitude. The high-performance OECTs fabricated by this method show promising applications in high-performance neuromorphic devices and ECG recording in advancing the field of electrochemical transistors.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface

Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.

Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures

Hybrid halide perovskites are attractive candidates for resistive switching memories in neuromorphic computing applications due to their mixed ionic-electronic conductivity. Moreover, their exceptional optoelectronic characteristics make them effective as semiconductors in photovoltaics, opening perspectives for self-powered memory elements. These devices, however, remain unexploited, which is related to the variability in their switching characteristics, weak endurance, and retention, which limit their performance and practical use. To address this challenge, we applied low-dimensional perovskite capping layers onto 3D mixed halide perovskites using two perfluoroarene organic cations, namely (perfluorobenzyl)ammonium and (perfluoro-1,4-phenylene)dimethylammonium iodide, forming Ruddlesden-Popper and Dion-Jacobson 2D perovskite phases, respectively. The corresponding mixed-dimensional perovskite heterostructures were used to fabricate resistive switching memories based on perovskite solar cell architectures, showing that the devices based on perfluoroarene heterostructures exhibited enhanced performance and stability in inert and ambient air atmosphere. This opens perspectives for multidimensional perovskite materials in durable self-powered memory elements in the future.

High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Humidity-Induced Protein-Based Artificial Synaptic Devices for Neuroprosthetic Applications

Neuroprosthetics and brain-machine interfaces are immensely beneficial for people with neurological disabilities, and the future generation of neural repair systems will utilize neuromorphic devices for the advantages of energy efficiency and real-time performance abilities. Conventional synaptic devices are not compatible to work in such conditions. The cerebrospinal fluid (CSF) in the central part of the nervous system is composed of 99% water. Therefore, artificial synaptic devices, which are the fundamental component of neuromorphic devices, should resemble biological nerves while being biocompatible, and functional in high-humidity environments with higher functional stability for real-time applications in the human body. In this work, artificial synaptic devices are fabricated based on gelatin-PEDOT: PSS composite as an active material to work more effectively in a highly humid environment (≈90% relative humidity). These devices successfully mimic various synaptic properties by the continuous variation of conductance, like, excitatory/inhibitory post-synaptic current(EPSC/IPSC), paired-pulse facilitation/depression(PPF/PPD), spike-voltage dependent plasticity (SVDP), spike-duration dependent plasticity (SDDP), and spike-rate dependent plasticity (SRDP) in environments at a relative humidity levels of ≈90%.

Humidity Adaptive Antifreeze Hydrogel Sensor for Intelligent Control and Human-Computer Interaction

Conductive hydrogels have emerged as ideal candidate materials for strain sensors due to their signal transduction capability and tissue-like flexibility, resembling human tissues. However, due to the presence of water molecules, hydrogels can experience dehydration and low-temperature freezing, which greatly limits the application scope as sensors. In this study, an ionic co-hybrid hydrogel called PBLL is proposed, which utilizes the amphoteric ion betaine hydrochloride (BH) in conjunction with hydrated lithium chloride (LiCl) thereby achieving the function of humidity adaptive. PBLL hydrogel retains water at low humidity (<50%) and absorbs water from air at high humidity (>50%) over the 17 days of testing. Remarkably, the PBLL hydrogel also exhibits strong anti-freezing properties (-80 °C), high conductivity (8.18 S m-1 at room temperature, 1.9 S m-1 at -80 °C), high gauge factor (GF approaching 5.1). Additionally, PBLL hydrogels exhibit strong inhibitory effects against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), as well as biocompatibility. By synergistically integrating PBLL hydrogel with wireless transmission and Internet of Things (IoT) technologies, this study has accomplished real-time human-computer interaction systems for sports training and rehabilitation evaluation. PBLL hydrogel exhibits significant potential in the fields of medical rehabilitation, artificial intelligence (AI), and the Internet of Things (IoT).

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System

The field of biomimetic electronics that mimic synaptic functions has expanded significantly to overcome the limitations of the von Neumann bottleneck. However, the scaling down of the technology has led to an increasingly intricate manufacturing process. To address the issue, this work presents a one-shot integrable electropolymerization (OSIEP) method with remote controllability for the deposition of synaptic elements on a chip by exploiting bipolar electrochemistry. Condensing synthesis, deposition, and patterning into a single fabrication step is achieved by combining alternating-current voltage superimposed on direct-current voltage-bipolar electropolymerization and a specially designed dual source/drain bipolar electrodes. As a result, uniform 6 × 5 arrays of poly(3,4-ethylenedioxythiophene) channels are successfully fabricated on flexible ultrathin parylene substrates in one-shot process. The channels exhibited highly uniform characteristics and are directly used as electrochemical synaptic transistor with synaptic plasticity over 100 s. The synaptic transistors have demonstrated promising performance in an artificial neural network (NN) simulation, achieving a high recognition accuracy of 95.20%. Additionally, the array of synaptic transistor is easily reconfigured to a multi-gate synaptic circuit to implement the principles of operant conditioning. These results provide a compelling fabrication strategy for realizing cost-effective and disposable NN systems with high integration density.

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.

Flexible Organic Transistors for Biosensing: Devices and Applications

Flexible and stretchable biosensors can offer seamless and conformable biological-electronic interfaces for continuously acquiring high-fidelity signals, permitting numerous emerging applications. Organic thin film transistors (OTFTs) are ideal transducers for flexible and stretchable biosensing due to their soft nature, inherent amplification function, biocompatibility, ease of functionalization, low cost, and device diversity. In consideration of the rapid advances in flexible-OTFT-based biosensors and their broad applications, herein, a timely and comprehensive review is provided. It starts with a detailed introduction to the features of various OTFTs including organic field-effect transistors and organic electrochemical transistors, and the functionalization strategies for biosensing, with a highlight on the seminal work and up-to-date achievements. Then, the applications of flexible-OTFT-based biosensors in wearable, implantable, and portable electronics, as well as neuromorphic biointerfaces are detailed. Subsequently, special attention is paid to emerging stretchable organic transistors including planar and fibrous devices. The routes to impart stretchability, including structural engineering and material engineering, are discussed, and the implementations of stretchable organic transistors in e-skin and smart textiles are included. Finally, the remaining challenges and the future opportunities in this field are summarized.

A Retina-Inspired Optoelectronic Synapse Using Quantum Dots for Neuromorphic Photostimulation of Neurons

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all-in-one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell-interfacing InP/ZnS quantum dots, which induces photo-faradaic charge-transfer mediated plasticity. The device sends excitatory post-synaptic currents exhibiting paired-pulse facilitation and post-tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post-synaptic currents. The results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments.

A Retina-Inspired Optoelectronic Synapse Using Quantum Dots for Neuromorphic Photostimulation of Neurons

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all-in-one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell-interfacing InP/ZnS quantum dots, which induces photo-faradaic charge-transfer mediated plasticity. The device sends excitatory post-synaptic currents exhibiting paired-pulse facilitation and post-tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post-synaptic currents. These results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics, and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments.

Visualized in-sensor computing

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Vertically Integrated Monolithic Neuromorphic Nanowire Device for Physiological Information Processing

This study demonstrates the conceptual design and fabrication of a vertically integrated monolithic (VIM) neuromorphic device. The device comprises an n-type SnO2 nanowire bottom channel connected by a shared gate to a p-type P3HT nanowire top channel. This architecture establishes two distinct neural pathways with different response behaviors. The device generates excitatory and inhibitory postsynaptic currents, mimicking the corelease mechanism of bilingual synapses. To enhance the signal processing efficiency, we employed a bipolar spike encoding strategy to convert fluctuating sensory signals to spike trains containing positive and negative pulses. Utilizing the neuromorphic platform for synaptic processing, physiological signals featuring bidirectional fluctuations, including electrocardiogram and breathing signals, can be classified with an accuracy of over 90%. The VIM device holds considerable promise as a solution for developing highly integrated neuromorphic hardware for healthcare and edge intelligence applications.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

1-Selector 1-Memristor Configuration with Multifunctional a-IGZO Memristive Devices Fabricated at Room Temperature

Serving as neuromorphic hardware accelerators, memristors play a crucial role in large-scale neuromorphic computing. Herein, two-terminal memristors utilizing amorphous indium-gallium-zinc oxide (a-IGZO) are fabricated through room-temperature sputtering. The electrical characteristics of these memristors are effectively modulated by varying the oxygen flow during the deposition process. The optimized a-IGZO memristor, fabricated under 3 sccm oxygen flow, presents a 5 × 103 ratio between its high- and low-resistance states, which can be maintained over 1 × 104 s with minimal degradation. Meanwhile, desirable properties such as electroforming-free and self-compliance, crucial for low-energy consumption, are also obtained in the a-IGZO memristor. Moreover, analog conductance switching is observed, demonstrating an interface-type behavior, as evidenced by its device-size-dependent performance. The coexistence of negative differential resistance with analog switching is attributed to the migration of oxygen vacancies and the trapping/detrapping of charges. Furthermore, the device demonstrates optical storage capabilities by exploiting the optical properties of a-IGZO, which can stably operate for up to 50 sweep cycles. Various synaptic functions have been demonstrated, including paired-pulse facilitation and spike-timing-dependent plasticity. These functionalities contribute to a simulated recognition accuracy of 90% for handwritten digits. Importantly, a one-selector one-memristor (1S1M) architecture is successfully constructed at room temperature by integrating a-IGZO memristor on a TaOx-based selector. This architecture exhibits a 107 on/off ratio, demonstrating its potential to suppress sneak currents among adjacent units in a memristor crossbar.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

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

Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors

Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains challenging. Here, utilizing a heterojunction of p- and n-type semiconductors coupled with separate floating-gate configuration, a Gaussian-like memory transistor is proposed, where a programmable Gaussian-like current-voltage response is achieved within a single device. A separate floating-gate structure allows for exquisite control of the Gaussian-like current output to a significant extent through simple programming, with an over 10000 s retention performance and mechanical flexibility. This enables physical evaluation of complex distribution functions with the simplified circuit design and higher parallelism. Successful implementation for localization and obstacle avoidance tasks is demonstrated using Gaussian-like curves produced from Gaussian-like memory transistor. With its ultralow-power consumption, simplified design, and programmable Gaussian-like outputs, our 3-terminal Gaussian-like memory transistor holds potential as a hardware platform for probabilistic inference computing.