One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory

Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.

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.

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.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Neuromorphic antennal sensory system

Insect antennae facilitate the nuanced detection of vibrations and deflections, and the non-contact perception of magnetic or chemical stimuli, capabilities not found in mammalian skin. Here, we report a neuromorphic antennal sensory system that emulates the structural, functional, and neuronal characteristics of ant antennae. Our system comprises electronic antennae sensor with three-dimensional flexible structures that detects tactile and magnetic stimuli. The integration of artificial synaptic devices adsorbed with solution-processable MoS2 nanoflakes enables synaptic processing of sensory information. By emulating the architecture of receptor-neuron pathway, our system realizes hardware-level, spatiotemporal perception of tactile contact, surface pattern, and magnetic field (detection limits: 1.3 mN, 50 μm, 9.4 mT). Vibrotactile-perception tasks involving profile and texture classifications were accomplished with high accuracy (> 90%), surpassing human performance in "blind" tactile explorations. Magneto-perception tasks including magnetic navigation and touchless interaction were successfully completed. Our work represents a milestone for neuromorphic sensory systems and biomimetic perceptual intelligence.

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.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

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.

Fiber Crossbars: An Emerging Architecture of Smart Electronic Textiles

Smart wearables have a significant impact on people's daily lives, enabling personalized motion monitoring, realizing the Internet of Things, and even reshaping the next generation of telemedicine systems. Fiber crossbars (FCs), constructed by crossing two fibers, have become an emerging architecture among the accessible structures of state-of-the-art smart electronic textiles. The mechanical, chemical, and electrical interactions between crossing fibers result in extensive functionalities, leading to the significant development of innovative electronic textiles employing FCs as their basic units. This review provides a timely and comprehensive overview of the structure designs, material selections, and assembly techniques of FC-based devices. The recent advances in FC-based devices are summarized, including multipurpose sensing, multiple-mode computing, high-resolution display, high-efficient power supply, and large-scale textile systems. Finally, current challenges, potential solutions, and future perspectives for FC-based systems are discussed for their further development in scale-up production and commercial applications.

An ultrasmall organic synapse for neuromorphic computing

High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as-fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle-to-cycle uniformity of 98.89% and device-to-device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking-plasticity-related algorithm for decision-making tasks.

Wide-Range Synaptic Current Responses with a Liquid Ga Electrode via a Surface Redox Reaction in a NaOH Solution at Different Molar Concentrations

A liquid Ga-based synaptic device with two-terminal electrodes is demonstrated in NaOH solutions at 50 °C. The proposed electrochemical redox device using the liquid Ga electrode in the NaOH solution can emulate various biological synapses that require different decay constants. The device exhibits a wide range of current decay times from 60 to 320 ms at different NaOH mole concentrations from 0.2 to 1.6 M. This research marks a step forward in the development of flexible and biocompatible neuromorphic devices that can be utilized for a range of applications where different synaptic strengths are required lasting from a few milliseconds to seconds.

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.

Nearly Panoramic Neuromorphic Vision with Transparent Photosynapses

Neuromorphic vision based on photonic synapses has the ability to mimic sensitivity, adaptivity, and sophistication of bio-visual systems. Significant advances in artificial photosynapses are achieved recently. However, conventional photosyanptic devices normally employ opaque metal conductors and vertical device configuration, performing a limited hemispherical field of view. Here, a transparent planar photonic synapse (TPPS) is presented that offers dual-side photosensitive capability for nearly panoramic neuromorphic vision. The TPPS consisting of all two dimensional (2D) carbon-based derivatives exhibits ultra-broadband photodetecting (365-970 nm) and ≈360° omnidirectional viewing angle. With its intrinsic persistent photoconductivity effect, the detector possesses bio-synaptic behaviors such as short/long-term memory, experience learning, light adaptation, and a 171% pair-pulse-facilitation index, enabling the synapse array to achieve image recognition enhancement (92%) and moving object detection.

Fibrous wearable and implantable bioelectronics

Fibrous wearable and implantable devices have emerged as a promising technology, offering a range of new solutions for minimally invasive monitoring of human health. Compared to traditional biomedical devices, fibers offer a possibility for a modular design compatible with large-scale manufacturing and a plethora of advantages including mechanical compliance, breathability, and biocompatibility. The new generation of fibrous biomedical devices can revolutionize easy-to-use and accessible health monitoring systems by serving as building blocks for most common wearables such as fabrics and clothes. Despite significant progress in the fabrication, materials, and application of fibrous biomedical devices, there is still a notable absence of a comprehensive and systematic review on the subject. This review paper provides an overview of recent advancements in the development of fibrous wearable and implantable electronics. We categorized these advancements into three main areas: manufacturing processes, platforms, and applications, outlining their respective merits and limitations. The paper concludes by discussing the outlook and challenges that lie ahead for fiber bioelectronics, providing a holistic view of its current stage of development.

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Integrated opposite charge grafting induced ionic-junction fiber

The emergence of ionic-junction devices has attracted growing interests due to the potential of serving as signal transmission and translation media between electronic devices and biological systems using ions. Among them, fiber-shaped iontronics possesses a great advantage in implantable applications owing to the unique one-dimensional geometry. However, fabricating stable ionic-junction on curved surfaces remains a challenge. Here, we developed a polyelectrolyte based ionic-junction fiber via an integrated opposite charge grafting method capable of large-scale continuous fabrication. The ionic-junction fibers can be integrated into functions such as ionic diodes and ionic bipolar junction transistors, where rectification and switching of input signals are implemented. Moreover, synaptic functionality has also been demonstrated by utilizing the fiber memory capacitance. The connection between the ionic-junction fiber and sciatic nerves of the mouse simulating end-to-side anastomosis is further performed to realize effective nerve signal conduction, verifying the capability for next-generation artificial neural pathways in implantable bioelectronics.

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

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

Soft Fiber Electronics Based on Semiconducting Polymer

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

Synaptic and Gradual Conductance Switching Behaviors in CeO2/Nb-SrTiO3 Heterojunction Memristors for Electrocardiogram Signal Recognition

The synaptic properties of memristors have been widely studied. However, researchers are still committed to solving various challenges, including the study of highly reliable memristors with comprehensive synaptic functions and memristors that simulate highly complex neurological learning rules. In this work, we report a CeO₂/Nb-SrTiO₃ heterojunction memristor whose conductance could be gradually tuned under both positive and negative pulse trains. Due to the gradual conductance switching behavior and the high switching ratio (10⁵), the CeO₂/Nb-SrTiO₃ heterojunction memristor could dutifully mimic biosynaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation and depression (PPF/PPD), spike amplitude-dependent plasticity (SADP), spike duration-dependent plasticity (SDDP), spike rate-dependent plasticity (SRDP), paired/triplet spiking-time-dependent plasticity (STDP), and Bienenstock-Cooper-Munro (BCM) rules. Moreover, a convolutional neural network based on the memristors is constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 93%. Besides, the recognition accuracy of the handwriting digit reaches 96%. These studies broaden the research scope of high-level synaptic behavior and lay a foundation for the future full synaptic memristor networks.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

A Machine-Learning-Enhanced Simultaneous and Multimodal Sensor Based on Moist-Electric Powered Graphene Oxide

Simultaneous multimodal monitoring can greatly perceive intricately multiple stimuli, which is important for the understanding and development of a future human-machine fusion world. However, the integrated multisensor networks with cumbersome structure, huge power consumption, and complex preparation process have heavily restricted practical applications. Herein, a graphene oxide single-component multimodal sensor (GO-MS) is developed, which enables simultaneous monitoring of multiple environmental stimuli by a single unit with unique moist-electric self-power supply. This GO-MS can generate a sustainable moist-electric potential by spontaneously adsorbing water molecules in air, which has a characteristic response behavior when exposed to different stimuli. As a result, the simultaneous monitoring and decoupling of the changes of temperature, humidity, pressure, and light intensity are achieved by this single GO-MS with machine-learning (ML) assistance. Of practical importance, a moist-electric-powered human-machine interaction wristband based on GO-MS is constructed to monitor pulse signals, body temperature, and sweating in a multidimensional manner, as well as gestures and sign language commanding communication. This ML-empowered moist-electric GO-MS provides a new platform for the development of self-powered single-component multimodal sensors, showing great potential for applications in the fields of health detection, artificial electronic skin, and the Internet-of-Things.

Integration of multiple electronic components on a microfibre towards an emerging electronic textile platform

Electronic fibres have been considered one of the desired device platforms due to their dimensional compatibility with fabrics by weaving with yarns. However, a precise connecting process between each electronic fibre is essential to configure the desired electronic circuits or systems. Here, we present an integrated electronic fibre platform by fabricating electronic devices onto a one-dimensional microfibre substrate. Electronic components such as transistors, inverters, ring oscillators, and thermocouples are integrated together onto the outer surface of a fibre substrate with precise semiconductor and electrode patterns. Our results show that electronic components can be integrated on a single fibre with reliable operation. We evaluate the electronic properties of the chip on the fibre as a multifunctional electronic textile platform by testing their switching and data processing, as well as sensing or transducing units for detecting optical/thermal signals. The demonstration of the electronic fibre suggests significant proof of concepts for the realization of high performance with wearable electronic textile systems.

Towards further integration and miniaturization of e-textiles, in this work, authors demonstrate an integrated electronic fibre platform by fabricating multiple electronic components such as transistors, inverters, ring oscillators, and thermocouples onto the outer surface of a microfibre substrate.

Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors

SignificanceEnabling distributed neurologic and cognitive functions in soft deformable devices, such as robotics, wearables, skin prosthetics, bioelectronics, etc., represents a massive leap in their development. The results presented here reveal the device characteristics of the building block, i.e., a stretchable elastomeric synaptic transistor, its characteristics under various levels of biaxial strain, and performances of various stretchy distributed neuromorphic devices. The stretchable neuromorphic array of synaptic transistors and the neuromorphic imaging sensory skin enable platforms to create a wide range of soft devices and systems with implemented neuromorphic and cognitive functions, including artificial cognitive skins, wearable neuromorphic computing, artificial organs, neurorobotics, and skin prosthetics.

Dynamic Model of the Short-Term Synaptic Behaviors of PEDOT-based Organic Electrochemical Transistors with Modified Shockley Equations

Neuromorphic computing is an emerging area with prospects to break the energy efficiency bottleneck of artificial intelligence (AI). A crucial challenge for neuromorphic computing is understanding the working principles of artificial synaptic devices. As an emerging class of synaptic devices, organic electrochemical transistors (OECTs) have attracted significant interest due to ultralow voltage operation, analog conductance tuning, mechanical flexibility, and biocompatibility. However, little work has been focused on the first-principal modeling of the synaptic behaviors of OECTs. The simulation of OECT synaptic behaviors is of great importance to understanding the OECT working principles as neuromorphic devices and optimizing ultralow power consumption neuromorphic computing devices. Here, we develop a two-dimensional transient drift-diffusion model based on modified Shockley equations for poly(3,4-ethylenedioxythiophene) (PEDOT)-based OECTs. We reproduced the typical transistor characteristics of these OECTs including the unique non-monotonic transconductance-gate bias curve and frequency dependency of transconductance. Furthermore, typical synaptic phenomena, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and short-term plasticity (STP), are also demonstrated. This work is crucial in guiding the experimental exploration of neuromorphic computing devices and has the potential to serve as a platform for future OECT device simulation based on a wide range of semiconducting materials.

2D Ultrawide Bandgap Semiconductors: Odyssey and Challenges

2D ultrawide bandgap (UWBG) semiconductors have aroused increasing interest in the field of high-power transparent electronic devices, deep-ultraviolet photodetectors, flexible electronic skins, and energy-efficient displays, owing to their intriguing physical properties. Compared with dominant narrow bandgap semiconductor material families, 2D UWBG semiconductors are less investigated but stand out because of their propensity for high optical transparency, tunable electrical conductivity, high mobility, and ultrahigh gate dielectrics. At the current stage of research, the most intensively investigated 2D UWBG semiconductors are metal oxides, metal chalcogenides, metal halides, and metal nitrides. This paper provides an up-to-date review of recent research progress on new 2D UWBG semiconductor materials and novel physical properties. The widespread applications, i.e., transistors, photodetector, touch screen, and inverter are summarized, which employ 2D UWBG semiconductors as either a passive or active layer. Finally, the existing challenges and opportunities of the enticing class of 2D UWBG semiconductors are highlighted.

Recent progress of fiber-based transistors: materials, structures and applications

Wearable electronics on fibers or fabrics assembled with electronic functions provide a platform for sensors, displays, circuitry, and computation. These new conceptual devices are human-friendly and programmable, which makes them indispensable for modern electronics. Their unique properties such as being adaptable in daily life, as well as being lightweight and flexible, have enabled many promising applications in robotics, healthcare, and the Internet of Things (IoT). Transistors, one of the fundamental blocks in electronic systems, allow for signal processing and computing. Therefore, study leading to integration of transistors with fabrics has become intensive. Here, several aspects of fiber-based transistors are addressed, including materials, system structures, and their functional devices such as sensory, logical circuitry, memory devices as well as neuromorphic computation. Recently reported advances in development and challenges to realizing fully integrated electronic textile (e-textile) systems are also discussed.

Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition

We fabricated a nanowire-channel intrinsically stretchable neuromorphic transistor (NISNT) that perceives both tactile and visual information and emulates neuromorphic processing capabilities. The device demonstrated excellent stretching endurance of 1000 stretch cycles while retaining stable electrical properties. The device was then applied as a multisensitive afferent nerve that processes information in parallel. Compatible with skin deformation, the devices are attached to fingers to serve as conformal strain sensors and neuromorphic information-processing units for gesture recognition. The excitatory postsynaptic current in each device represents shape changes and is then analyzed using softmax activation processing of the neural network to recognize gestures. A multistage neural network that uses NISNT was used to further confirm the gestures. This work demonstrated an idea toward multisensory artificial nerves and neuromorphic systems.

Dynamic Self-Rectifying Liquid Metal-Semiconductor Heterointerfaces: A Platform for Development of Bioinspired Afferent Systems

The assembly of geometrically complex and dynamically active liquid metal/semiconductor heterointerfaces has drawn extensive attention in multidimensional electronic systems. In this study the chemovoltaic driven reactions have enabled the microfluidity of hydrophobic galinstan into a three-dimensional (3D) semiconductor matrix. A dynamic heterointerface is developed between the atomically thin surface oxide of galinstan and the TiO₂-Ni interface. Upon the growth of Ga₂O₃ film at the Ga₂O₃-TiO₂ heterointerface, the partial reduction of the TiO₂ film was confirmed by material characterization techniques. The conductance imaging spectroscopy and electrical measurements are used to investigate the charge transfer at heterointerfaces. Concurrently, the dynamic conductance in artificial synaptic junctions is modulated to mimic the biofunctional communication characteristics of multipolar neurons, including slow and fast inhibitory and excitatory postsynaptic responses. The self-rectifying characteristics, femtojoule energy processing, tunable synaptic events, and notably the coordinated signal recognition are the main characteristics of this multisynaptic device. This novel 3D design of liquid metal-semiconductor structure opens up new opportunities for the development of bioinspired afferent systems. It further facilitates the realization of physical phenomena at liquid metal-semiconductor heterointerfaces.

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

We propose a flexible artificial synapse based on a silicon-indium-zinc-oxide (SIZO)/ion gel hybrid structure directly fabricated on a polyimide substrate, where the channel conductance is effectively modulated via ion movement in the ion gel. This synaptic operation is possible because of the low-temperature deposition process of the SIZO layer (<150°C) and the surface roughness improvement of the poly(4-vinylphenol) buffer layer (12.29→1.81 nm). The flexible synaptic device exhibits extremely stable synaptic performance under high mechanical (bending 1500 times with a radius of 5 mm) and electrical stress (application of voltage pulses 10⁴ times) without any degradation. Last, a sensory-neuromorphic system for sign language translation, which consists of stretchable resistive sensors and flexible artificial synapses, is designed and successfully evaluated via training and recognition simulation using hand sign patterns obtained by stretchable sensors (maximum recognition rate, 99.4%).

Applications of Carbon Nanotubes in the Internet of Things Era

The post-Moore's era has boosted the progress in carbon nanotube-based transistors. Indeed, the 5G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices. In this perspective, we deliver the readers with the latest trends in carbon nanotube research, including high-frequency transistors, biomedical sensors and actuators, brain-machine interfaces, and flexible logic devices and energy storages. Future opportunities are given for calling on scientists and engineers into the emerging topics.

Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications

Fiber electronics is a key research area for realizing wearable microelectronic devices. Significant progress has been made in recent years in developing the geometry and composition of electronic fibers. In this review, we present that recent progress in the architecture and electrical properties of electronic fibers, including their fabrication methods. We intensively investigate the structural designs of fiber-shaped devices: coaxial, twisted, three-dimensional layer-by-layer, and woven structures. In addition, we introduce remarkable applications of fiber-shaped devices for energy harvesting/storage, sensing, and light-emitting devices. Electronic fibers offer high potential for use in next-generation electronics, such as electronic textiles and smart integrated textile systems, which require excellent deformability and high operational reliability.

Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning

Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.

Electronic Textiles Fabricated with Graphene Oxide-Coated Commercial Textiles

Demand for wearable and portable electronic devices has increased, raising interest in electronic textiles (e-textiles). E-textiles have been produced using various materials including carbon nanotubes, graphene, and graphene oxide. Among the materials in this minireview, we introduce e-textiles fabricated with graphene oxide (GO) coating, using commercial textiles. GO-coated cotton, nylon, polyester, and silk are reported. The GO-coated commercial textiles were reduced chemically and thermally. The maximum e-textile conductivity of about 10 S/cm was achieved in GO-coated silk. We also introduce an e-textile made of uncoated silk. The silk-based e-textiles were obtained using a simple heat treatment with axial tension. The conductivity of the e-textiles was over 100 S/cm.

An Artificial Electrical-Chemical Mixed Synapse Based on Ion-Gated MoS2 Nanosheets for Real-Time Facilitation Index Tuning

Tunability of facilitation in short-term memory (STM) provides great potential in bioinspired computing. Recently, several doping strategies were proposed to modify the intrinsic features of materials, resulting in the optimization of the facilitation index (FI). However, real-time scale tuning, which is implemented on the same synaptic device, has not yet been demonstrated. Inspired by the chemical-electrical mixed synapse structure in the brain, we propose a three-terminal artificial synapse based on an ion-gated MoS₂ memristor. The gate terminal serves as a nonvolatile ionic pump via chemical intercalation, which effectively affects both the conductance baseline and the hysteresis degree of the STM effect of the memristor. We further modeled the postsynaptic current (PSC) behavior and used it for reservoir computing. Simulation results show that, due to the real-time tuning ability, the built reservoir can be programmed for specific handwritten recognition tasks with the pruning of neurons from 784 to 50. The developed artificial mixed synapse is promising for a downsampling module in neural network design.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Selective Release of Different Neurotransmitters Emulated by a p-i-n Junction Synaptic Transistor for Environment-Responsive Action Control

The design of the first p-i-n junction synaptic transistor (JST) based on n-type TiO2 film covered with poly(methyl methacrylate) (PMMA) and with a p-type P3HT/PEO nanowire (NW) on top. Except for basic synaptic functions that can be realized by a single neurotransmitter, the electronic device emulates the multiplexed neurotransmission of different neurotransmissions, i.e., glutamate and acetylcholine, for fast switching between short- and long-term plasticity (STP and LTP). This is realized by the special p-i-n junction with hole transport in the p-type P3HT NW to form STP, and electron transport in the n-type TiO2 layer and trapped under the PMMA inversion layer to form LTP. Altering the external input induces changes of the polarity of the charge carriers in the conductive channel, promoting fast switching between STP and LTP modes. When stimulated using two parallel inputs, the response of PMMA/TiO2 emulates the synergistic effect of taste and aroma on the control of food-intake in the brain. Because of the bipolarity, the p-i-n JST has excellent reconfigurability, which importantly is attributed to simulate the plasticity of synapses and to mimic how distinct types of gustatory receptor neurons respond to different concentrations of salt. The electronic device lays the technical foundation for the realization of the future complex artificial neural networks.