Mechanoplastic Tribotronic Floating‐Gate Neuromorphic Transistor

The emulation of synaptic plasticity to achieve sophisticated cognitive functions and adaptive behaviors is critical to the evolution of neuromorphic computation and artificial intelligence. More feasible plastic strategies (e.g., mechanoplasticity) are urgent to achieve comparable, versatile, and active cognitive complexity in neuromorphic systems. Here, a versatile mechanoplastic artificial synapse based on tribotronic floating‐gate MoS₂ synaptic transistors is proposed. Mechanical displacement can induce triboelectric potential coupling to the floating‐gate synaptic transistor, trigger a postsynaptic current signal, and modulate the synaptic weights, which realizes the synaptic mechanoplasticity in an active and interactive way. Typical synaptic plasticity behaviors including potentiation/inhibition and paired pulse facilitation/depression are successfully imitated. Assistant with the charge trapping by floating gate, the artificial synapse can realize mechanical displacement derived short‐term and long‐term plasticity simultaneously. A facile artificial neural network is also constructed to demonstrate an adding synaptic weight and neuromorphic logic switching (AND, OR) by mechanoplasticity without building complex complementary metal oxide semiconductor circuits. The proposed mechanoplastic artificial synapse offers a favorable candidate for the construction of mechanical behavior derived neuromorphic devices to overcome the von Neumann bottleneck and perform advanced synaptic behaviors.     

Piezotronic Graphene Artificial Sensory Synapse

The human somatosensory system, consisting of receptors, transmitters, and synapses, functions as the medium for external mechanical stimuli perception and sensing signal delivery/processing. Developing sophisticated artificial sensory synapses with a high performance, uncomplicated fabrication process, and low power consumption is still a great challenge. Here, a piezotronic graphene artificial sensory synapse developed by integrating piezoelectric nanogenerator (PENG) with an ion gel–gated transistor is demonstrated. The piezopotential originating from PENG can efficiently power the synaptic device due to the formation of electrical double layers at the interface of the ion gel/electrode and ion gel/graphene. Meanwhile, the piezopotential coupling is capable of linking the spatiotemporal strain information (strain amplitude and duration) with the postsynaptic current. The synaptic weights can be readily modulated by the strain pulses. Typical properties of a synapse including excitation/inhibition, synaptic plasticity, and paired pulse facilitation are successfully demonstrated. The dynamic modulation of a sensory synapse is also achieved based on dual perceptual presynaptic PENGs coupling to a single postsynaptic transistor. This work provides a new insight into developing piezotronic synaptic devices in neuromorphic computing, which is of great significance in future self‐powered electronic skin with artificial intelligence, a neuromorphic interface for neurorobotics, human–robot interaction, an intelligent piezotronic transistor, etc.     

Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti3C2Tx MXenes Floating Gate

The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.     

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Designing transparent flexible electronics with multi-biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel-gated VO₂ Mott transistor is developed to simulate the functions of the biological synapse. Short-term and long-term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton-doping modulation, respectively. With the achievement of multi-essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle-to-cycle variations of multi-conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically-controlled VO₂ metal-insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.     

2D Material Based Synaptic Devices for Neuromorphic Computing

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high-performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.     

Sub-Femtojoule-Energy-Consumption Conformable Synaptic Transistors Based on Organic Single-Crystalline Nanoribbons

Inspired from powerful functionalities of human brain, artificial synapses are innovated continuously for the construction of brain-like neuromorphic electronics. The quest to rival the ultralow energy consumption of biological synapses has become highly compelling, but remains extremely difficult due to the lack of appropriate materials and device construction. In this study, organic single-crystalline nanoribbon active layer and elastic embedded photolithographic electrodes are first designed in synaptic transistors to reduce energy consumption of single device. The minimum energy consumption (0.29 fJ) of one synaptic event is far lower than that of biological synapse (10 fJ). Notably, sub-femtojoule-energy-consumption synaptic transistors can simulate various biological plastic behaviors even under different tensile and compressive strains, offering a new guidance for the construction of ultralow-energy-consuming neuromorphic electronic devices and the development of flexible artificial intelligence electronics in the future.     

Synaptic plasticity modulation and coincidence detection emulated in multi-terminal neuromorphic transistors

Human brain is a powerful biological computer that can processing a large number of cognitive tasks simultaneously. Inspired by our brain, many emerging devices have been developed for neuromorphic computing and perception in recent years. Due to the interfacial electron/ion coupling, electric-double-layer (EDL) transistors gated by electrolytes are promising candidates for neuromorphic devices. Here, we demonstrate a multi-terminal indium-tin-oxide (ITO)-based EDL transistor gated by chitosan electrolyte and this device exhibits good electrical properties. Short-term synaptic plasticity modulation and neuron functions (temporal integration, coincidence detection) are investigated. Our results indicate that oxide-based EDL transistors are promising for neuromorphic application.     

Artificial Synapse Based on Oxygen Vacancy Migration in Ferroelectric-Like C-Axis-Aligned Crystalline InGaSnO Semiconductor Thin-Film Transistors for Highly Integrated Neuromorphic Electronics

Artificial synapses are a key component of neuromorphic computing systems. To achieve high-performance neuromorphic computing ability, a huge number of artificial synapses should be integrated because the human brain has a huge number of synapses (≈10¹⁵). In this study, a coplanar synaptic, thin-film transistor (TFT) made of c-axis-aligned crystalline indium gallium tin oxide (CAAC–IGTO) is developed. The electrical characteristics of the biological synapses such as inhibitory postsynaptic current (IPSC), paired-pulse depression (PPD), short-term plasticity (STP), and long-term plasticity at V_DS = 0.1 V, are demonstrated. The measured synaptic behavior can be explained by the migration of positively charged oxygen vacancies (V_o⁺/V_o⁺⁺) in the CAAC–IGTO layer. The mechanism of implementing synaptic behavior is completely new, compared to previous reports using electrolytes or ferroelectric gate insulators. The advantage of this device is to use conventional gate insulators such as SiO₂ for synaptic behavior. Previous studies use chitosan, Ta₂O₃, SiO₂ nanoparticles , Gd₂O₃, and HfZrO_x for gate insulators, which cannot be used for high integration of synaptic devices. The metal–oxide TFTs, widely used in the display industry, can be applied to the synaptic transistors. Therefore, CAAC–IGTO synaptic TFT can be a good candidate for application as an artificial synapse for highly integrated neuromorphic chips.     

Ultrasensitive Freestanding and Mechanically Durable Artificial Synapse with Attojoule Power Based on Na-Salt Doped Polymer for Biocompatible Neuromorphic Interface

The human brain, with high energy-efficient and parallel processing ability, inspires to mitigate power issues perplexing von Neumann architecture. As one of the essential components constructing the human brain, the emulation of biological synapses exploiting electronic devices consuming power at a biological level lays the foundation for the implementation of energy-efficient neuromorphic computing. Besides, signal matching between biologically-related stimuli and the driving voltage of artificial synapses helps to realize intelligent neuromorphic interfaces and sustainable energy. Here, ultra-sensitive artificial synapse stimulated at 1 mV with energy consumption of 132 attojoule/synaptic event is demonstrated. Biological signal matching and low power application are realized simultaneously based on sodium acetate (NaAc) doped polyvinyl alcohol (PVA) electrolyte. The biphasic current, which comprises the electrical- and ion-mediation current component, contributes to enrich synaptic functions compared to monophasic synaptic behavior. Moreover, freestanding NaAc-doped PVA membrane functions as both dielectric layer and mechanical support and facilitates to achieve flexible, transferable artificial synapse, which maintains functional stability at an ultralow voltage and power even after bending tests. Thus, encompassing superior sensitivity, low energy, and multiple functionalities with flexible, self-supported, biocompatible property, takes a step to construct energetically-efficient, complex neuromorphic systems for wearable, implantable medicines as well as smart bio-electronic interfaces.     

Printed Neuromorphic Devices Based on Printed Carbon Nanotube Thin‐Film Transistors

Hardware implementation of artificial synapse/neuron by electronic/ionic hybrid devices is of great interest for brain‐inspired neuromorphic systems. At the same time, printed electronics have received considerable interest in recent years. Here, printed dual‐gate carbon‐nanotube thin‐film transistors with very high saturation field‐effect mobility (≈269 cm² V^?1 s^–1) are proposed for artificial synapse application. Some important synaptic behaviors including paired‐pulse facilitation (PPF), and signal filtering characteristics are successfully emulated in such printed artificial synapses. The PPF index can be modulated by spike width and spike interval of presynaptic impulse voltages. The results present a printable approach to fabricate artificial synaptic devices for neuromorphic systems.     

A Reliable All-2D Materials Artificial Synapse for High Energy-Efficient Neuromorphic Computing

High-performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy-efficient neuromorphic computing. Here, a two-terminal floating-gate memory (2TFGM) based artificial synapse built from all-2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all-2D 2TFGM for high-speed and low-power neuromorphic computing.     

硫系非晶态半导体人工神经突触的可塑性

模拟大脑中的神经突触是实现下一代计算机——类脑神经形态计算的关键一步。为了利用光子模拟神经突触的可塑性进而发展全光人工神经突触器件,文章开展了基于可控光诱导抑制效应的硫系非晶态半导体人工神经突触的实验研究。分析了材料化学组分和抽运光功率对该人工神经突触的调控作用,描述了该人工神经突触的可塑性。结果表明掺入不同杂质的硫系非晶态半导体平面波导具有不同的可控光诱导抑制过程,且抑制深度受控于抽运光功率的变化。基于这些特性,该人工神经突触展现出了配对脉冲易化功能、短程抑制功能、长程抑制功能,具有良好的可塑性。     

Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike‐Timing‐Dependent Plasticity

Neuromorphic and cognitive computing with a capability of analyzing complicated information is explored as a new paradigm of intelligent systems. An implementation of a renewable material as an essential building block of an artificial synaptic device is suggested and a flexible and transparent synaptic device based on collagen extracted from fish skin is demonstrated. This device exhibits essential synaptic behaviors including analog memory characteristics, excitatory postsynaptic current, and paired‐pulse facilitation as short‐term plasticity. The brain‐inspired electronic synapse undergoes incremental potentiation and depression when flat or bent. The device emulates spike‐timing‐dependent plasticity when stimulated by engineered pre‐ and post‐neuron spikes with the appropriate time difference between the imposed pulses. The proposed synaptic device has the advantage of being biocompatible owing to use of Mg electrodes and collagen as a naturally abundant protein. This device has a potential to be used in flexible and implantable neuromorphic systems in the future.     

Electrolyte‐Gated Synaptic Transistor with Oxygen Ions

Artificial synaptic devices are the essential hardware of neuromorphic computing systems, which can simultaneously perform signal processing and information storage between two neighboring artificial neurons. Emerging electrolyte‐gated transistors have attracted much attention for efficient synaptic emulation by using an addition gate terminal. Here, an electrolyte‐gated synaptic device based on the SrCoO_x (SCO) films is proposed. It is demonstrated that the reversible modulation of SCO phase transforms the brownmillerite SrCoO_2.5 and perovskite SrCoO_3?δ , through controlling the insertion and extraction of oxygen ions with electrolyte gating. Nonvolatile multilevel conduction states can be realized in the SCO films following this route. The synaptic functions such as the long‐term potentiation and depression of synaptic weight, spike‐timing‐dependent plasticity, as well as spiking logic operations in the device are successfully mimicked. These results provide an alternative avenue for future neuromorphic devices via electrolyte‐gated transistors with oxygen ions.     

Bio-Inspired Artificial Perceptual Devices for Neuromorphic Computing and Gesture Recognition

Artificial perception technologies capable of sensing and feeling mechanical stimuli like human skins are critical enablers for electronic skins (E-Skins) needed to achieve artificial intelligence. However, most of the reported electronic skin systems lack the capability to process and interpret the sensor data. Herein, a new design of artificial perceptual system integrating ZnO-based synaptic devices with Pt/carbon nanofibers-based strain sensors for stimuli detection and information processing is presented. Benefiting from the controllable ion migration after indium doping, the device can emulate various essential functions, such as short-term/long-term plasticity, paired-pulse facilitation, excitatory post-synaptic current, and synaptic plasticity depending on the number, frequency, amplitude, and width of the applied pulses. The Pt/carbon nanofibers-based strain sensors can detect subtle human motion and convert mechanical stimuli into electrical signals, which are further processed by the ZnO devices. By attaching the integrated devices to finger joints, it is demonstrated that they can recognize handwriting and gestures with a high accuracy. This work offers new insights in designing artificial synapses and sensors to process and recognize information for neuromorphic computing and artificial intelligence applications.     

Recent Advances in Transistor‐Based Artificial Synapses

Simulating biological synapses with electronic devices is a re‐emerging field of research. It is widely recognized as the first step in hardware building brain‐like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor‐based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor‐based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor‐based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor‐based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor‐based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor‐based artificial synapses are also presented.     

Bio-Inspired Artificial Fast-Adaptive and Slow-Adaptive Mechanoreceptors With Synapse-Like Functions

Development of artificial mechanoreceptors capable of sensing and pre-processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio-inspired artificial fast-adaptive (FA) and slow-adaptive (SA) mechanoreceptors with synapse-like functions are demonstrated for tactile perception. These mechanoreceptors integrate self-powered piezoelectric pressure sensors with synaptic electrolyte-gated field-effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric-ionic coupling effect based on P(VDF-TrFE) and an ionic liquid. Changes in post-synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre-synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.     

Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.     

Mimicking Neuroplasticity in a Hybrid Biopolymer Transistor by Dual Modes Modulation

Neuromorphic computing systems that are capable of parallel information storage and processing with high area and energy efficiencies, offer important opportunities for future storage systems and in‐memory computing. Here, it is shown that a carbon dots/silk protein (CDs/silk) blend can be used as a light‐tunable charge trapping medium to fabricate an electro‐photoactive transistor synapse. The synaptic device can be optically operated in volatile or nonvolatile modes, ensuring concomitant short‐term and long‐term neuroplasticity. The synaptic‐like behaviors are attributed to the photogating effect induced by trapped photogenerated electrons in the hybrid CDs/silk film which is confirmed with atomic force microscopy based electrical techniques. In addition, system‐level pattern recognition capability of the synaptic device is evaluated by a single‐layer perceptron model. The remote optical operation of neuromorphic architecture provides promising building blocks to complete bioinspired photonic computing paradigms.     

Linear Classification Function Emulated by Pectin-Based Polysaccharide-Gated Multiterminal Neuron Transistors

Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi-input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi-terminal pectin-gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in-plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.