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.     

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.     

Transparent and Flexible Inorganic Perovskite Photonic Artificial Synapses with Dual-Mode Operation

With the rapid development of artificial intelligence, the simulation of the human brain for neuromorphic computing has demonstrated unprecedented progress. Photonic artificial synapses are strongly desirable owing to their higher neuron selectivity, lower crosstalk, wavelength multiplexing capabilities, and low operating power compared to their electric counterparts. This study demonstrates a highly transparent and flexible artificial synapse with a two-terminal architecture that emulates photonic synaptic functionalities. This optically triggered artificial synapse exhibits clear synaptic characteristics such as paired-pulse facilitation, short/long-term memory, and synaptic behavior analogous to that of the iris in the human eye. Ultraviolet light illumination-induced neuromorphic characteristics exhibited by the synapse are attributed to carrier trapping and detrapping in the SnO₂ nanoparticles and CsPbCl₃ perovskite interface. Moreover, the ability to detect deep red light without changes in synaptic behavior indicates the potential for dual-mode operation. This study establishes a novel two-terminal architecture for highly transparent and flexible photonic artificial synapse that can help facilitate higher integration density of transparent 3D stacking memristors, and make it possible to approach optical learning, memory, computing, and visual recognition.     

Tuning the synaptic behaviors of biocompatible synaptic transistor through ion-doping

Biodegradable and environmentally friendly artificial synapse devices are essential for the future development of neuromorphic computing. The emergence of synaptic transistors based on biocompatible polymer materials provides an ideal approach to achieve green electronics. However, modulating the synaptic properties in a wide range in a fixed biocompatible synaptic transistor is still challengeable, while it is vitally important for improving the adaptability of the synaptic device to achieve neuro-prosthetics in the future. Here, we reported the regulation of the synaptic behavior of biocompatible synaptic transistor through ion-doping, which allows to adjusting the response of the synaptic device according to a specific function. The ions doped into the insulating layer strengthen the formation of electric double layers (EDLs), which enables a remarkable regulation effect on post-synaptic current. Moreover, basic synaptic properties, including excitatory/inhibitory post-synaptic current (EPCS/IPSC), paired-pulse facilitation/depression (PPF/PPD), short-term/long-term memory (STM/LTM) are successfully demonstrated. In addition, high-pass and low-pass filtering functions are also implemented in a single synaptic device, indicating that the synapse attenuation can be effectively transformed according to the needs of the function. More importantly, this is the first work to demonstrate that the accuracy of pattern recognition of synaptic transistors, an important indicator of neuromorphic calculations, can be significantly improved via ion doping (as high as 75.96% relative to undoped devices of 41.68%). Our research provides a feasible strategy for precisely controlling synaptic behaviors, which has a profound impact on improving the adaptability of artificial synaptic devices in the field of neuromorphic computing.     

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.     

An artifical synapse based on graphene field-effect transistor with silver gel/polarized-aptamer gate

In this work, we present an artificial synapse based on side-gate graphene-field-effect-transistor (GFET) using biocompatible silver gel/polarized-aptamer as gate dielectric. The gate functions as the presynaptic membrane, while the drain works as postsynaptic membrane. Various synaptic plasticities, including short-term enhancement (STE), short-term depression (STD), long-term potentiation (LTP), long-term depression (LTD) and the transformation from short-term plasticity to long-term plasticity have been emulated by the GFET artificial synapse. A model based on the function of a difference of two exponentials that is widely used to model the biological synapses is proposed, well fitting the behavior of the fabricated artificial synapses under different presynaptic spikes. With a fixed current of −1 μA applied to postsynaptic membrane, the excitatory-postsynaptic-potential-like spikes can be generated at postsynaptic membrane under the positive spikes applied to presynaptic membrane, suggesting the similarities between the artificial and biological synapses.     

Tuning a Binary Ferromagnet into a Multistate Synapse with Spin–Orbit‐Torque‐Induced Plasticity

Ferromagnets with binary states are limited for applications as artificial synapses for neuromorphic computing. Here, it is shown how synaptic plasticity of a perpendicular ferromagnetic layer (FM1) can be obtained when it is interlayer exchange‐coupled by another in‐plane ferromagnetic layer (FM2), where a magnetic field‐free current‐driven multistate magnetization switching of FM1 in the Pt/FM1/Ta/FM2 structure is induced by spin–orbit torque. Current pulses are used to set the perpendicular magnetization state, which acts as the synapse weight, and spintronic implementation of the excitatory/inhibitory postsynaptic potentials and spike timing‐dependent plasticity are demonstrated. This functionality is made possible by the action of the in‐plane interlayer exchange coupling field which leads to broadened, multistate magnetic reversal characteristics. Numerical simulations, combined with investigations of a reference sample with a single perpendicular magnetized Pt/FM1/Ta structure, reveal that the broadening is due to the in‐plane field component tuning the efficiency of the spin–orbit torque to drive domain walls across a landscape of varying pinning potentials. The conventionally binary FM1 inside the Pt/FM1/Ta/FM2 structure with an inherent in‐plane coupling field is therefore tuned into a multistate perpendicular ferromagnet and represents a synaptic emulator for neuromorphic computing, demonstrating a significant pathway toward a combination of spintronics and synaptic electronics.     

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.     

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。     

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.     

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.     

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 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.     

Artificial Optoelectronic Synapses Based on TiNxO2–x/MoS2 Heterojunction for Neuromorphic Computing and Visual System

Being capable of dealing with both electrical signals and light, artificial optoelectronic synapses are of great importance for neuromorphic computing and are receiving a burgeoning amount of interest in visual information processing. In this work, an artificial optoelectronic synapse composed of Al/TiN_xO_2–x/MoS₂/ITO (H-OSD) is proposed and experimentally realized. The H-OSD can enable basic electrical voltage-induced synaptic functions such as the long/short-term plasticity and moreover the synaptic plasticity can be electrically adjusted. In response to the light stimuli, versatile advanced synaptic functions including long/short-term memory, and learning-forgetting-relearning are successfully demonstrated, which could enhance the information processing capability for neuromorphic computing. Most importantly, based on these light-induced salient features, a 4 × 4 synapse array is developed to show the potential application of the proposed H-OSD in constructing artificial visual system. It is shown that the perceiving and memorizing of the light information that are respectively relevant to the visual perception and visual memory functions, can be readily attained through tuning of the light intensity and the number of illuminations. As such, the proposed optoelectronic synapse shows great potentials in both neuromorphic computing and visual information processing and will facilitate the applications such as electronic eyes and light-driven neurorobotics.     

Neuromorphic Synapses for Artificial Dendrites

We describe neuromorphic, variable-weight synapses onartificial dendrites that facilitate experimentation with correlativeadaptation rules. Attention is focused on those aspects of biologicalsynaptic function that could affect a neuromorphic network'scomputational power and adaptive capability. These include sublinearsummation, quantal synaptic noise, and independent adaptationof adjacent synapses. The diffusive nature of artificial dendritesadds considerable flexibility to the design of fast synapsesby allowing conductances to be enabled for short or for variablelengths of time. We present two complementary synapse designs:the shared conductance array and the self-timed synapse. Bothsynapse circuits behave as conductances to mimic biological synapsesand thus enable sublinear summation. The former achieves weightvariation by selecting different conductances from an on-chiparray, and the latter by modulating the length of time a constantconductance remains activated. Both work with our interchip communicationsystem, virtual wires. For the present purpose of comparing variousadaptation mechanisms in software, synaptic weights are storedoff chip. This simplifies the addition of quantal weight noiseand allows connections from different sources to the same dendriticcompartment to have independent weights.     

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.     

Tailoring Neuroplasticity in a Ferroelectric-Gated Multi-Terminal Synaptic Transistor by Bi-Directional Modulation for Improved Pattern Edge Recognition

The dynamic modulation of the plasticity of artificial neuromorphic devices facilitates a wide range of neuromorphic functions. However, integrating diverse plasticity modulation techniques into a single device presents a challenge due to limitations in the device structure design. Here, a multiterminal artificial synaptic device capable of bi-directional modulation on its plasticity is proposed. Significantly, the conversion of inhibitory and excitatory synaptic plasticity can be achieved not only by modifying the polarity of the presynaptic voltage spike but also by exchanging its input terminal between top and bottom gate while maintaining the same presynaptic stimuli. This unique bi-directional modulation of synaptic plasticity has been attributed to two distinct physical mechanisms: nonvolatile ferroelectric polarization and interface charge trap-induced memory characteristics. Additionally, the effective dynamic modulation of the synaptic behaviors is quantified under different back-gate bias and verified in the constructed neural network perceptron. Further, a visual simulation demonstrates the enhanced clarity and precision of edge recognition through the back-gate modulation in the artificial synapses. This study provides a strategy to fulfill diversified modulation on synaptic plasticity in ferroelectric-gated transistors, thereby prompting efficient and controllable neuromorphic visual systems.     

Silver‐Adapted Diffusive Memristor Based on Organic Nitrogen‐Doped Graphene Oxide Quantum Dots (N‐GOQDs) for Artificial Biosynapse Applications

Carbon‐based electronic devices are suitable candidates for bioinspired electronics due to their low cost, eco‐friendliness, mechanical flexibility, and compatibility with complementary metal‐oxide‐semiconductor technology. New types of materials such as graphene quantum dots (GQDs) have attracted attention in the search for new applications beyond solar cells and energy harvesting due to their superior properties such as elevated photoluminescence, high chemical inertness, and excellent biocompatibility. In this paper, a biocompatible/organic electronic synapse based on nitrogen‐doped graphene oxide quantum dots (N‐GOQDs) is reported, which exhibits threshold resistive switching via silver cation (Ag⁺) migration dynamics. In analogy to the calcium (Ca²⁺) ion dynamics of biological synapses, important biological synapse functions such as short‐term potentiation (STP), paired‐pulse facilitation, and transition from STP to long‐term plasticity behaviors are replicated. Long‐term depression behavior is also evaluated and specific spike‐timing dependent plasticity is assessed. In addition, elaborated switching mechanism of biosimilar Ag⁺ migration dynamics provides the potential for using N‐GOQD‐based artificial synapse in future biocompatible neuromorphic systems.     

Ion-gel gated field-effect transistors with solution-processed oxide semiconductors for bioinspired artificial synapses

With the development of sciences and technologies of artificial intelligence in recent years, more and more attention is focused on the research of the synaptic devices inspired by human brain. In this paper, ion-gel coupled synaptic transistors with solution-possessed amorphous indium-zinc-oxide (In-Zn-O) thin films were demonstrated. Ion-gel dielectric provides a strong ionic/electronic coupling on the solution-processed In-Zn-O thin films, which is due to the very large electric-double-layer (EDL) capacitances (∼4.87 μF/cm²). The surface morphology, chemical composition/stoichiometry and electrical performances of In-Zn-O field-effect transistors (FETs) were analyzed as a function of annealing temperature. Most importantly, the ion-gel gated In-Zn-O FETs were used for synaptic functions simulations. The in-plane gate is used as the presynaptic input terminal and the In-Zn-O channel with source/drain electrodes is used as the postsynaptic output terminal. Mobile ions in ion-gel are regarded as neurotransmitters. Gate pluses were applied on the in-plane electrodes which is analogous to presynaptic spikes onto presynaptic membrane. Fundamental synaptic functions including excitatory postsynaptic current (EPSC), spike time-dependent EPSC, paired-pulse facilitation (PPF), and dynamic synaptic behaviors are mimicked. These results may provide a new opportunity and strategy to develop of highly functional electronic synapses and next-generation neuromorphic systems by using ion-gel gated devices with solution-processed amorphous oxide semiconductors.     

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.