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.     

Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based arti?cial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS₂ grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS₂ grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning.     

Flexible Memristor Constructed by 2D Cadmium Phosphorus Trichalcogenide for Artificial Synapse and Logic Operation

The development of advanced microelectronics requires new device architecture and multi-functionality. Low-dimensional material is considered as a powerful candidate to construct new devices. In this work, a flexible memristor is fabricated utilizing 2D cadmium phosphorus trichalcogenide nanosheets as the functional layer. The memristor exhibits excellent resistive switching performance under different radius and over 10³ bending times. The device mechanism is systematically investigated, and the synaptic plasticity including paired-pulse facilitation and spiking timing-dependent plasticity are further observed. Furthermore, based on the linearly conductance modulation capacity of the flexible memristor, the applications on decimal operation are explored, that the addition, subtraction, multiplication, and division of decimal calculation are successfully achieved. These results demonstrate the potential of metal phosphorus trichalcogenide in novel flexible neuromorphic devices, which accelerate the application process of neuromorphic computing.     

Compensated Ferrimagnet Based Artificial Synapse and Neuron for Ultrafast Neuromorphic Computing

Spintronic devices are considered a possible solution for the hardware implementation of artificial synapses and neurons, as a result of their non-volatility, high scalability, complementary metal-oxide-semiconductor transistor compatibility, and low power consumption. As compared to ferromagnets, ferrimagnet-based spintronics exhibits equivalently fascinating properties that have been witnessed in ultrafast spin dynamics, together with efficient electrical or optical manipulation. Their applications in neuromorphic computing, however, have still not been revealed, which motivates the present experimental study. Here, by using compensated ferrimagnets containing Co_0.80Gd_0.20 with perpendicular magnetic anisotropy, it is demonstrated that the behavior of spin-orbit torque switching in compensated ferrimagnets could be used to mimic biological synapses and neurons. In particular, by using the anomalous Hall effect and magneto-optical Kerr effect imaging measurements, the ultrafast stimulation of artificial synapses and neurons is illustrated, with a time scale down to 10 ns. Using experimentally derived device parameters, a three-layer fully connected neural network for handwritten digits recognition is further simulated, based on which, an accuracy of more than 93% could be achieved. The results identify compensated ferrimagnets as an intriguing candidate for the ultrafast neuromorphic spintronics.     

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.     

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.     

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two-Dimensional Materials Integration

In-memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time-consuming challenges associated with the von Neumann architecture. The ferroelectric field-effect transistor (FeFET) technology, with its fast and energy-efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study,  the capabilities of an integrated ferroelectric HfO₂ and 2D MoS₂ channel FeFET in achieving high-performance 4-bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET-based in-memory computing for future neuromorphic computing applications.     

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.     

Artificial Helical Screws of 2D Materials with Archimedean Spiral Arrangement

Helix structures, which are frequently observed in nature, act as versatile structural templates for complex functionalities with asymmetry and anisotropy. However, atomically thin 2D materials, including graphene, transition metal dichalcogenides (TMDs), and MXenes, do not have inherent chirality in their planar geometry and cannot easily form such a structure. This study presents the macroscopic self-assembly of 2D materials for helical screws with an Archimedean spiral arrangement. The naturally triggered spontaneous rotation upon the 1D fiber assembly of 2D materials forms helical screws consisting of multiple helices and perversions. For a clear understanding of the morphological evolution of helical screws, variations in the helical pitch and angle are systematically analyzed considering thermodynamic and kinetic conditions. Subsequently, the influence of spontaneous helix formation on the properties of the 2D assembled fibers is investigated in terms of the solvent-driven actuator performance and electrical and electrothermal properties. The suggested approach provides a new perspective on the directed self-assembly of inherently achiral 2D materials toward chiral helix formation.     

HfO2-Based Memristor as an Artificial Synapse for Neuromorphic Computing with Tri-Layer HfO2/BiFeO3/HfO2 Design

Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal–oxide semiconductor devices in recent times. In this work, tri-layer HfO₂/BiFeO₃(BFO)/HfO₂ memristors are designed by inserting traditional ferroelectric BFO layers measuring ≈4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10⁴ and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.     

Defective Engineering Tuning the Analog Switching Linearity and Symmetry of Two-Terminal Artificial Synapse for Neuromorphic Systems

Neuromorphic computing inspired by memristors has gained considerable attention due to its low power and easy integration. However, state-of-the-art two-terminal resistive switching memristors based on conductive filament formation suffer from high variability and poor controllability. As a three-terminal device operated through electrochemistry and dynamic insertion/extraction of ions, the electrochemical ion synapse demonstrates deterministic control of electron conductivity based on ion doping. But, integrating the electrochemical ion synapse into crossbar arrays will pose higher challenges and lower integration density. Herein, inspired by first-principles calculations, a two-terminal bidirectional plasticity electrochemical artificial synapse with integrated lithium polymer electrolyte and polycrystalline tungsten oxide layer is reported. The linearity and stability of the device weight update are greatly improved by adjusting the defect concentration of the polycrystalline WO₃ layer. Even after 16 000 write-read events in the air, its performance remained almost unchanged. Moreover, it has an over-limit protection mechanism under one-way stimulation that exceeds the normal range. Based on this excellent stability, the authors designed and successfully simulated the “muscle memory” that the programmatical organization of the nervous system leads to proficiency in specific actions.     

Emerging MXene-Based Memristors for In-Memory,Neuromorphic Computing,and Logic Operation

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data-intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in-memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high-performance memristors. Herein, the review focuses on the emerging 2D MXene materials-based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene-based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene-based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene-based memristors and more engaging information technology applications.     

Atomically Thin MoS2: A Versatile Nongraphene 2D Material

Two‐dimensional inorganic materials are emerging as a premiere class of materials for fabricating modern electronic devices. The interest in 2D layered transition metal dichalcogenides is especially high. Particularly, 2D MoS₂ is being heavily researched due to its novel functionalities and its suitability for a wide range of electronic and optoelectronic applications. In this article, the progress in mono/few layer(s) MoS₂ research is reviewed by focusing primarily on the layer dependent evolution of crystal, phonon, and electronic structure. The review includes extensive detail into the methodologies adapted for single or few layer(s) MoS₂ growth. Further, the review covers the versatility of 2D MoS₂ for a broad range of device applications. Recent advancements in the field of van der Waals heterostructures are also highlighted at the end of the review.     

Stacking of 2D Materials

2D layered materials have sparked great interest from the perspective of basic physics and applied science in the past few years. Extraordinarily, many novel stacked structures that bring versatile properties and applications can be artificially assembled, as exemplified by vertical van der Waals (vdW) heterostructures, twisted multilayer 2D materials, hybrid dimensional structures, etc. Compared with the ordinary synthesis process, the stacking technique is a powerful strategy to achieve high-quality and freely controlled 2D material stacked structures with atomic accuracy. This review highlights the most advanced stacking techniques involving the preparation, transfer, and stacking of high-quality single crystal 2D materials. Apart from the 2D–2D stacked structures, 2D–0D, 2D–1D, and 2D–3D structures offer a prospective platform for the increasing application of 2D materials. The assembly strategy and physical properties of these stacked structures strongly depend on the factors in the stacking process, including the surface quality, angle control, and sample size. In addition, comparative analysis tables on the techniques involved are also available. The summary of these strategies and techniques will hopefully provide a valuable reference for relevant work.     

2D Organic–Inorganic Hybrid Thin Films for Flexible UV–Visible Photodetectors

Flexible 2D inorganic MoS₂ and organic g‐C₃N₄ hybrid thin film photodetectors with tunable composition and photodetection properties are developed using simple solution processing. The hybrid films fabricated on paper substrate show broadband photodetection suitable for both UV and visible light with good responsivity, detectivity, and reliable and rapid photoswitching characteristics comparable to monolayer devices. This excellent performance is retained even after the films are severely deformed at a bending radius of ≈2 mm for hundreds of cycles. The detailed charge transfer and separation processes at the interface between the 2D materials in the hybrid films are confirmed by femtosecond transient absorption spectroscopy with broadband capability.     

Laterally Heterogeneous 2D Layered Materials as an Artificial Light‐Harvesting Proton Pump

Heterogeneous structures in nacre‐mimetic 2D layered materials generate novel transport phenomena in angstrom range, and thus provide new possibilities for innovative applications for sustainable energy, a clean environment, and human healthcare. In the two orthogonal transport directions, either vertical or horizontal, heterostructures in horizontal direction have never been reported before. Here, a 2D‐material‐based laterally heterogeneous membrane is fabricated via an unconventional dual‐flow filtration method. Negatively and positively charged graphene oxide multilayers are laterally patterned and interconnected in a planar configuration. Upon visible light illumination on the bipolar nanofluidic heterojunction, protons are able to move uphill against their concentration gradient, functioning as a light‐harvesting proton pump. A maximum proton concentration gradient of about 5.4 pH units mm^?2 membrane area can be established at a transport rate up to 14.8 mol h^?1 m^?2. The transport mechanism can be understood as a light‐triggered asymmetric polarization in surface potential and the consequent change in proton capacity in separate parts. The implementation of photonic–ionic conversion with abiotic materials provides a full‐solid‐state solution for bionic vision and artificial photosynthesis. There is plenty of room to expect the laterally heterogeneous membranes for new functions and better performance in the abundant family of liquid processable colloidal 2D materials.     

基于铁电材料的有机光电突触晶体管研究

模拟生物突触结构的设备是实现神经网络计算的可行方案之一,其中人工视网膜器件为机器视觉和图像识别的实现提供了有力支持。通过旋涂制备聚偏氟乙烯-三氟乙烯(P(VDF-TrFE))制备铁电栅层,热蒸发酞菁铜(CuPc)作为半导体层,探究该晶体管模拟突触功能的光电响应。实验结果表明,该光电晶体管在625 nm具有显著的光响应,其能够产生兴奋性突触后电流(EPSC)并实现短期可塑性到长期可塑性的转变以及高通滤波功能。利用剩余极化强度模拟了大脑学习过程中提前施加注意的行为。此外,以栅电压和光照作为独立输入逻辑信号,在单个晶体管中实现了“与”和“或”的布尔逻辑功能。上述结果表明,CuPc可以与铁电材料进行良好结合并制备出具有突触响应特点的光电晶体管,这为人工视网膜器件的开发提供了有机铁电器件的参考。     

Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing

Memristor, based on the principle of biological synapse, is recognized as one of the key devices in confronting the bottleneck of classical von Neumann computers. However, conventional memristors are difficult to continuously adjust the conduction and dutifully mimic the biosynapse function. Here, TiO₂ films with self‐assembled Ag nanoclusters implemented by gradient Ag dopant are employed to achieve enhanced memristor performance. The memristors exhibit gradual both potentiating and depressing conduction under positive and negative pulse trains, which can fully emulate excitation and inhibition of biosynapse. Moreover, comprehensive biosynaptic functions and plasticity, including the transition from short‐term memory to long‐term memory, long‐term potentiation and depression, spike‐timing‐dependent plasticity, and paired‐pulse facilitation, are implemented with the fabricated memristors in this work. The applied pulses with a width of hundreds of nanoseconds timescale are beneficial to realize fast learning and computing. High‐resolution transmission electron microscopy observations clearly demonstrate that Ag clusters redistribute to form Ag conductive filaments between Ag and Pt electrode under electrical field at ON‐state device. The experimental data confirm that the oxides doped with Ag clusters have the potential for mimicking biosynaptic behavior, which is essential for the further creation of artificial neural systems.