Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human–brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.     

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.     

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics

The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.     

Stimuli‐Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

Brain‐inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli‐enabled synapses will be important for future neuromorphic computing applications. In this Review, state‐of‐the‐art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox‐based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.     

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.     

Low‐Power,Electrochemically Tunable Graphene Synapses for Neuromorphic Computing

Brain‐inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal–oxide–semiconductor devices (with “0” and “1” states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (<500 fJ per switching event), analog tunability (>250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long‐term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two‐dimensional synapse is scalable in terms of switching energy and speed.     

Field‐Driven Athermal Activation of Amorphous Metal Oxide Semiconductors for Flexible Programmable Logic Circuits and Neuromorphic Electronics

Despite extensive research, large‐scale realization of metal‐oxide electronics is still impeded by high‐temperature fabrication, incompatible with flexible substrates. Ideally, an athermal treatment modifying the electronic structure of amorphous metal oxide semiconductors (AMOS) to generate sufficient carrier concentration would help mitigate such high‐temperature requirements, enabling realization of high‐performance electronics on flexible substrates. Here, a novel field‐driven athermal activation of AMOS channels is demonstrated via an electrolyte‐gating approach. Facilitating migration of charged oxygen species across the semiconductor–dielectric interface, this approach modulates the local electronic structure of the channel, generating sufficient carriers for charge transport and activating oxygen‐compensated thin films. The thin‐film transistors (TFTs) investigated here depict an enhancement of linear mobility from 51 to 105.25 cm² V^?1 s^?1 (ionic‐gated) and from 8.09 to 14.49 cm² V^?1 s^?1 (back‐gated), by creating additional oxygen vacancies. The accompanying stochiometric transformations, monitored via spectroscopic measurements (X‐ray photoelectron spectroscopy) corroborate the detailed electrical (TFT, current evolution) parameter analyses, providing critical insights into the underlying oxygen‐vacancy generation mechanism and clearly demonstrating field‐induced activation as a promising alternative to conventional high‐temperature annealing strategies. Facilitating on‐demand active programing of the operation modes of transistors (enhancement vs depletion), this technique paves way for facile fabrication of logic circuits and neuromorphic transistors for bioinspired computing.     

The Design of 3D‐Interface Architecture in an Ultralow‐Power,Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human–machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid‐ion gated organic field‐effect transistors (OFETs) with a 3D‐interface conducting channel for ultralow‐power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and high‐pass filtering, are successfully realized. Furthermore, the single‐fiber based artificial synapse can be operated by an ultralow presynaptic spike down to ?0.5 mV with an ultralow reading voltage at ?0.1 mV due to the large contact surface between the ionic electrolyte and fiber‐like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low‐power integrated synaptic circuit.     

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.     

Flexible and Stretchable Antennas for Biointegrated Electronics

Combined advances in material science, mechanical engineering, and electrical engineering form the foundations of thin, soft electronic/optoelectronic platforms that have unique capabilities in wireless monitoring and control of various biological processes in cells, tissues, and organs. Miniaturized, stretchable antennas represent an essential link between such devices and external systems for control, power delivery, data processing, and/or communication. Applications typically involve a demanding set of considerations in performance, size, and stretchability. Some of the most effective strategies rely on unusual materials such as liquid metals, nanowires, and woven textiles or on optimally configured 2D/3D structures such as serpentines and helical coils of conventional materials. In the best cases, the performance metrics of small, stretchable, radio frequency (RF) antennas realized using these strategies compare favorably to those of traditional devices. Examples range from dipole, monopole, and patch antennas for far-field RF operation, to magnetic loop antennas for near-field communication (NFC), where the key parameters include operating frequency, Q factor, radiation pattern, and reflection coefficient S₁₁ across a range of mechanical deformations and cyclic loads. Despite significant progress over the last several years, many challenges and associated research opportunities remain in the development of high-efficiency antennas for biointegrated electronic/optoelectronic systems.     

Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems

The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.     

Flexible Sensing Electronics for Wearable/Attachable Health Monitoring

Wearable or attachable health monitoring smart systems are considered to be the next generation of personal portable devices for remote medicine practices. Smart flexible sensing electronics are components crucial in endowing health monitoring systems with the capability of real‐time tracking of physiological signals. These signals are closely associated with body conditions, such as heart rate, wrist pulse, body temperature, blood/intraocular pressure and blood/sweat bio‐information. Monitoring such physiological signals provides a convenient and non‐invasive way for disease diagnoses and health assessments. This Review summarizes the recent progress of flexible sensing electronics for their use in wearable/attachable health monitoring systems. Meanwhile, we present an overview of different materials and configurations for flexible sensors, including piezo‐resistive, piezo‐electrical, capacitive, and field effect transistor based devices, and analyze the working principles in monitoring physiological signals. In addition, the future perspectives of wearable healthcare systems and the technical demands on their commercialization are briefly discussed.     

Printable Transparent Conductive Films for Flexible Electronics

Printed electronics are an important enabling technology for the development of low‐cost, large‐area, and flexible optoelectronic devices. Transparent conductive films (TCFs) made from solution‐processable transparent conductive materials, such as metal nanoparticles/nanowires, carbon nanotubes, graphene, and conductive polymers, can simultaneously exhibit high mechanical flexibility, low cost, and better photoelectric properties compared to the commonly used sputtered indium‐tin‐oxide‐based TCFs, and are thus receiving great attention. This Review summarizes recent advances of large‐area flexible TCFs enabled by several roll‐to‐roll‐compatible printed techniques including inkjet printing, screen printing, offset printing, and gravure printing using the emerging transparent conductive materials. The preparation of TCFs including ink formulation, substrate treatment, patterning, and postprocessing, and their potential applications in solar cells, organic light‐emitting diodes, and touch panels are discussed in detail. The rational combination of a variety of printed techniques with emerging transparent conductive materials is believed to extend the opportunities for the development of printed electronics within the realm of flexible electronics and beyond.     

Flexible Hybrid Electronics for Digital Healthcare

Recent advances in material innovation and structural design provide routes to flexible hybrid electronics that can combine the high-performance electrical properties of conventional wafer-based electronics with the ability to be stretched, bent, and twisted to arbitrary shapes, revolutionizing the transformation of traditional healthcare to digital healthcare. Here, material innovation and structural design for the preparation of flexible hybrid electronics are reviewed, a brief chronology of these advances is given, and biomedical applications in bioelectrical monitoring and stimulation, optical monitoring and treatment, acoustic imitation and monitoring, bionic touch, and body-fluid testing are described. In conclusion, some remarks on the challenges for future research of flexible hybrid electronics are presented.