Flexible artificial Si-In-Zn-O/ion gel synapse and its application to sensory-neuromorphic system for sign language translation

Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception

With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.

Toward an AI Era: Advances in Electronic Skins

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

Energy-Efficient Reservoir Computing Based on Solution-Processed Electrolyte/Ferroelectric Memcapacitive Synapses for Biosignal Classification

The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems. This work presents solution-processed electrolyte/ferroelectric memcapacitive synapses. Leveraging the synergistic coupling of electrical double-layer (EDL) effects and ferroelectric polarization, these synapses exhibit tunable long- and short-term plasticity, ultralow power consumption (∼27 fJ per spike), and rich reservoir state dynamics, making them well-suited for energy-efficient RC systems. The classifications of critical electrocardiogram (ECG) signals, including arrhythmia and obstructive sleep apnea (OSA), are performed using the synapse-based RC system, demonstrating excellent accuracies of 97.8 and 80.0% for arrhythmia and OSA classifications, respectively. These findings pave the way for developing lightweight, energy-efficient machine-learning platforms for biosignal classification in wearable devices.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing

In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.

Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy

Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.

Gel-Based Triboelectric Nanogenerators for Flexible Sensing: Principles, Properties, and Applications

The rapid development of the Internet of Things and artificial intelligence technologies has increased the need for wearable, portable, and self-powered flexible sensing devices. Triboelectric nanogenerators (TENGs) based on gel materials (with excellent conductivity, mechanical tunability, environmental adaptability, and biocompatibility) are considered an advanced approach for developing a new generation of flexible sensors. This review comprehensively summarizes the recent advances in gel-based TENGs for flexible sensors, covering their principles, properties, and applications. Based on the development requirements for flexible sensors, the working mechanism of gel-based TENGs and the characteristic advantages of gels are introduced. Design strategies for the performance optimization of hydrogel-, organogel-, and aerogel-based TENGs are systematically summarized. In addition, the applications of gel-based TENGs in human motion sensing, tactile sensing, health monitoring, environmental monitoring, human-machine interaction, and other related fields are summarized. Finally, the challenges of gel-based TENGs for flexible sensing are discussed, and feasible strategies are proposed to guide future research.

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.

Dual Interactive Mode Human-Machine Interfaces Based on Triboelectric Nanogenerator and IGZO/In2O3 Heterojunction Synaptic Transistor

Imitating human neural networks via bio-inspired electronics advances human-machine interfaces (HMI), overcoming von Neumann limitations and enabling efficient, low-energy data processing in the big data era. However, single-contact mode HMIs have inherent limitations in terms of their capabilities and performances, such as constrained adaptability to dynamic environments, and reduced cognitive processing capabilities. Here, a dual-interactive-mode HMI system based on a triboelectric nanogenerator (TENG) and heterojunction synaptic transistor (HJST) is proposed for both contact and non-contact applications. The TENG incorporates a poly-methyl meth-acrylate (PMMA)-NiCo2S4/S film, in which the NiCo2S4/S composite traps and blocks electrons to optimize charge generation and storage. The heterojunction structure, mitigates the Debye screening effect, thereby improving transistor characteristics and reliability. The integrated TENG-HJST system exhibits synaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and synaptic plasticity, enabling emulation of neural behavior and advanced information processing. Moreover, neural morphology manipulation is demonstrated in practical tasks, such as controlling international chess games. By integrating the TENG-HJST device with a robotic hand, conscious artificial responses are generated, enhancing event accuracy. This breakthrough in dual-interactive-mode interfacing holds promise for HMI systems and neural prostheses.

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.

Gradiently Foaming Ultrasoft Hydrogel with Stop Holes for Highly Deformable, Crack-Resistant and Sensitive Conformal Human-Machine Interfaces

Hydrogels are considered as promising materials for human-machine interfaces (HMIs) owing to their merits of tailorable mechanical and electrical properties; nevertheless, it remains challenging to simultaneously achieve ultrasoftness, good mechanical robustness and high sensitivity, which are the pre-requisite requirements for wearable sensing applications. Herein, for the first time, this work proposes a universal phase-transition-induced bubbling strategy to fabricate ultrasoft gradient foam-shaped hydrogels (FSHs) with stop holes for high deformability, crack-resistance and sensitive conformal HMIs. As a typical system, the FSH based on polyacrylamide/sodium alginate system shows an ultralow Young's modulus (1.68 kPa), increased sustainable strain (1411%), enhanced fracture toughness (915.6 J m-2 ), improved tensile sensitivity (21.77), and compressive sensitivity (65.23 kPa-1 ). The FSHs are used for precisely acquiring and identifying gesture commands of the operator to remotely control a surgical robot for endoscopy and an electric ship in a first-person perspective for cruising, feeding crabs and monitoring the environmental change in real-time.

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.

Toward a Brain-Neuromorphics Interface

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

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Artificial Neuron Devices

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

Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces

Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.

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

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

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Wearable Smart Bandage-Based Bio-Sensors

Bandage is a well-established industry, whereas wearable electronics is an emerging industry. This review presents the bandage as the base of wearable bioelectronics. It begins with introducing a detailed background to bandages and the development of bandage-based smart sensors, which is followed by a sequential discussion of the technical characteristics of the existing bandages, a more practical methodology for future applications, and manufacturing processes of bandage-based wearable biosensors. The review then elaborates on the advantages of basing the next generation of wearables, such as acceptance by the customers and system approvals, and disposal.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS2 Channel/LiSiOx Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing

Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS₂ channel and LiSiO_x (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS₂ channel and LiSiO_x solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.

Pseudo-transistors for emerging neuromorphic electronics

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

Machine Learning-Enhanced Flexible Mechanical Sensing

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Emerging electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing

With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.

Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing

The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.

Balanced Coexistence of Reversible and Irreversible Covalent Bonds in a Conductive Triple Polymeric Network Enables Stretchable Hydrogels with High Toughness and Adhesiveness

The application of soft hydrogels to stretchable devices has attracted increasing attention in deformable bioelectronics owing to their unique characteristic, "modulus matching between materials and organs". Despite considerable progress, their low toughness, low conductivity, and absence of tissue adhesiveness remain substantial challenges associated with unstable skin-interfacing, where body movements undesirably disturb electrical signal acquisitions. Herein, we report a material design of a highly tough strain-dissipative and skin-adhesive conducting hydrogel fabricated through a facile one-step sol-gel transition and its application to an interactive human-machine interface. The hydrogel comprises a triple polymeric network where irreversible amide linkage of polyacrylamide with alginate and dynamic covalent bonds entailing conjugated polymer chains of poly(3,4-ethylenedioxythiophene)-co-(3-thienylboronic acid) are simultaneously capable of high stretchability (1300% strain), efficient strain dissipation (36,209 J/m²), low electrical resistance (590 Ω), and even robust skin adhesiveness (35.0 ± 5.6 kPa). Based on such decent characteristics, the hydrogel was utilized as a multifunctional layer for successfully performing either electrophysiological cardiac/muscular on-skin sensors or an interactive stretchable human-machine interface.

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

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

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

Ferroelectric Field-Effect-Transistor Integrated with Ferroelectrics Heterostructure

To address the demands of emerging data-centric computing applications, ferroelectric field-effect transistors (Fe-FETs) are considered the forefront of semiconductor electronics owing to their energy and area efficiency and merged logic-memory functionalities. Herein, the fabrication and application of an Fe-FET, which is integrated with a van der Waals ferroelectrics heterostructure (CuInP₂ S₆ /α-In₂ Se₃ ), is reported. Leveraging enhanced polarization originating from the dipole coupling of CIPS and α-In₂ Se₃ , the fabricated Fe-FET exhibits a large memory window of 14.5 V at V_GS = ±10 V, reaching a memory window to sweep range of ≈72%. Piezoelectric force microscopy measurements confirm the enhanced polarization-induced wider hysteresis loop of the double-stacked ferroelectrics compared to single ferroelectric layers. The Landau-Khalatnikov theory is extended to analyze the ferroelectric characteristics of a ferroelectric heterostructure, providing detailed explanations of the hysteresis behaviors and enhanced memory window formation. The fabricated Fe-FET shows nonvolatile memory characteristics, with a high on/off current ratio of over 10⁶ , long retention time (>10⁴ s), and stable cyclic endurance (>10⁴ cycles). Furthermore, the applicability of the ferroelectrics heterostructure is investigated for artificial synapses and for hardware neural networks through training and inference simulation. These results provide a promising pathway for exploring low-dimensional ferroelectronics.

Mixed-Dimensional Formamidinium Bismuth Iodides Featuring In-Situ Formed Type-I Band Structure for Convolution Neural Networks

For valence change memory (VCM)-type synapses, a large number of vacancies help to achieve very linearly changed dynamic range, and also, the low activation energy of vacancies enables low-voltage operation. However, a large number of vacancies increases the current of artificial synapses by acting like dopants, which aggravates low-energy operation and device scalability. Here, mixed-dimensional formamidinium bismuth iodides featuring in-situ formed type-I band structure are reported for the VCM-type synapse. As compared to the pure 2D and 0D phases, the mixed phase increases defect density, which induces a better dynamic range and higher linearity. In addition, the mixed phase decreases conductivity for non-paths despite a large number of defects providing lots of conducting paths. Thus, the mixed phase-based memristor devices exhibit excellent potentiation/depression characteristics with asymmetricity of 3.15, 500 conductance states, a dynamic range of 15, pico ampere-scale current level, and energy consumption per spike of 61.08 aJ. A convolutional neural network (CNN) simulation with the Canadian Institute for Advanced Research-10 (CIFAR-10) dataset is also performed, confirming a maximum recognition rate of approximately 87%. This study is expected to lay the groundwork for future research on organic bismuth halide-based memristor synapses usable for a neuromorphic computing system.