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.     

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.     

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.     

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。     

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.     

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.     

All-Optically Controlled Artificial Synapse Based on Full Oxides for Low-Power Visible Neural Network Computing

Artificial synapse devices are dedicated to overcoming the von Neumann bottleneck. Adopting light signals in visual information processing and computing is vital for developing next-generation artificial neuromorphic systems. A strategy to construct all-optically controlled artificial synaptic devices based on full oxides with amorphous ZnAlSnO/SnO heterojunction in a two-terminal planar configuration is proposed. All synaptic behaviors are operated in the visible optical pathway, with excitatory synapse under red (635 nm) light and inhibitory synapse under green (532 nm) and blue (405 nm) lights. Based on the different inhibitory effects, two modes of long-term depression (LTD) and RESET processes can be implemented through green and blue lights, respectively. The energy consumption of an event can be as low as 0.75 pJ. A three-layer perceptron model is designed to classify 28 × 28-pixel handwritten digital images and performed supervised learning using a backpropagation algorithm, demonstrating the bio-visually inspired neuromorphic computing with a training accuracy of 92.74%. The all-optically controlled artificial synapses with write/erasure behaviors in visible RGB region and rational microelectronic process, as presented in this work, are essential in developing future artificial neuromorphic systems and highlight the huge potential of next-generation computer systems.     

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.     

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.     

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.     

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.     

Broadband Optoelectronic Synapse Enables Compact Monolithic Neuromorphic Machine Vision for Information Processing

Traditional machine vision is suffering from redundant sensing data, bulky structures, and high energy consumption. Biological-inspired neuromorphic systems are promising for compact and energy-efficient machine vision. Multifunctional optoelectronics enabling multispectrum sensitivity for broadband image sensing, feature extraction, and neuromorphic computing are vital for machine visions. Here, an optoelectronic synapse is designed that enables image sensing, convolutional processing, and computing. Multiple synaptic plasticity triggered by photons can implement photonic computing and information transmission. Convolutional processing is realized by ultralow energy kernel generators fully controlled by photons. Meanwhile, the device shows the ability of conductance modulations under electronic stimulations that implement neuromorphic computing. For the first time, this two-terminal broadband optoelectronic synapse enables front-end retinomorphic image sensing, convolutional processing, and back-end neuromorphic computing. The integrated photonic information encryption, convolutional image preprocessing, and neuromorphic computing capabilities are promising for compact monolithic neuromorphic machine vision systems.     

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.     

Electrolyte-gated transistors for neuromorphic applications

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck.To enhance computing performance,neuromorphic computing systems that can simulate the function of the human brain are being developed.Artificial synapses are essential electronic devices for neuromorphic architectures,which have the ability to perform signal processing and storage between neighboring artificial neurons.In recent years,electrolyte-gated transistors(EGTs)have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications.Among the various electronic devices,EGT-based artificial synapses offer the benefits of good stability,ultra-high linearity and repeated cyclic symmetry,and can be constructed from a variety of materials.They also spatially separate“read”and“write”operations.In this article,we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications.We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses.Then,we review different types of channels and electrolyte materials for EGT-based artificial synapses.Finally,we review the potential applications in biological functions.     

Electronic synapses mimicked in bilayer organic-inorganic heterojunction based memristor

Electronic synapses implementing in-memory computing system could overcome the developing limitation on the energy efficiency of traditional von Neumann architecture. Compared with the high sensitivity of biological synapses, lower responsivity of the memristive synapses was found via the electrical stimulations. Here, poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6- tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)-dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl} (PDPPBTT)/zinc oxide (ZnO) based heterojunction is found to exhibit stable memristive switching behavior, which originates from the confined formation/rupture of filament in the two-layer interface region as the ions migrate with different transport rates in two layers. The implementing synaptic functions in the sensitive memristive device can realize the short-term plasticity and long-term plasticity when stimulated by the applied electrical signals with different stimulating rate. Similar to the biological synapse, the memory loss, memory transition, and the critical role of stimulation rate on the transition process, can be achieved in the as-prepared memristor device. The systematic demonstrations on the synaptic emulation may facilitate building bio-inspired device-level neuromorphic systems.     

Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse

The development of electronic devices that possess the functionality of biological synapses is a crucial step towards neuromorphic computing.In this work,we present a WOx-based memristive device that can emulate voltage-dependent synaptic plasticity.By adjusting the amplitude of the applied voltage,we were able to reproduce short-term plasticity(STP)and the transition from STP to long-term potentiation.The stimulation with high intensity induced long-term enhancement of conductance without any decay process,thus representing a permanent memory behavior.Moreover,the image Boolean operations(including intersection,subtraction,and union)were also demonstrated in the memristive synapse array based on the above voltage-dependent plasticity.The experimental achievements of this study provide a new insight into the successful mimicry of essential characteristics of synaptic behaviors.     

Nanowires for UV–vis–IR Optoelectronic Synaptic Devices

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non-contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)-based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires-based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs-based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs-based optoelectronic synaptic devices.     

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.     

Set/Reset Bilaterally Controllable Resistance Switching Ga-doped Ge2Sb2Te5 Long-Term Electronic Synapses for Neuromorphic Computing

Long-term plasticity of bio-synapses modulates the stable synaptic transmission that is quite related to the encoding of information and its emulation using electronic hardware is one of important targets for neuromorphic computing. Ge₂Sb₂Te₅ (GST) based phase change random access memory (PCRAM) has become a strong candidate for complementary-metal-oxide-semiconductor (CMOS) compatible integrated long-term electronic synapses to cope with the high-efficient and low power consumption data processing tasks for neuromorphic computing. However, the performance of PCRAM electronic synapses is still quite limited due to the challenges in linear and continuous conductance regulation, which originates from the fast and uncontrollable resistance switching characteristic of conventional PCRAM for the data storage application. Here an in-depth study is reported on the impact of gallium (Ga) doping on GST (GaGST) structural properties and on the corresponding 0.13 µm CMOS technology fabricated PCRAM integrated devices with a mushroom structure. The Ga doping effectively retarded the crystallization process of GST by augmenting the local disorder of GeTe_4-nGe_n tetrahedron, which subsequently leads to the Set/Reset bilaterally controllable resistance switching of corresponding PCRAM devices. The optimized 6.5%GaGST electronic synapses demonstrate gradual resistance switching characteristics and a good multilevel retention feature and eventually exhibit outstanding long-term synaptic plasticity like potentiation/depression and spiking time dependent plasticity in four forms. Such long-term electronic synapses are applied to handwritten digits recognition (96.22%) and CIFAR-10 image categorization (93.6%) and attain very high accuracy for both tasks. These results provide an effective method to achieve high performance PCRAM electronic synapses and highlight the great potential of GaGST PCRAM as a component for future high-performance neuromorphic computing.     

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.