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

Advancements in Nanowire-Based Devices for Neuromorphic Computing: A Review

Neuromorphic computing, inspired by the highly interconnected and energy-efficient way the human brain processes information, has emerged as a promising technology for post-Moore's law era. This emerging technology can emulate the structures and the functions of the human brain and is expected to overcome the fundamental limitation of the current von Neumann computing architecture. Neuromorphic devices stand out as the key components of future electronic systems, exhibiting potential in shaping the landscape of neuromorphic computing. Especially, nanowire (NW)-based neuromorphic devices, with their advantages of high integration, high-speed computing, and low power consumption, have recently emerged as candidates for neuromorphic computing technology. Here, a critical overview of the current development and relevant research in the field of NW-based neuromorphic devices are provided. Neuromorphic devices based on different NW materials are comprehensively discussed, including Ag NW-based, organic NW-based, metal oxide NW-based, and semiconductor NW-based devices. Finally, as a foresight perspective, the potentials and the challenges of these NW-based neuromorphic devices for use as future brain-like electronics are discussed.

Ultra-Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe2 for Neuromorphic Computing

Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe2) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.

Synthesis, Modulation, and Application of Two-Dimensional TMD Heterostructures

Two-dimensional (2D) transition metal dichalcogenide (TMD) heterostructures have attracted a lot of attention due to their rich material diversity and stack geometry, precise controllability of structure and properties, and potential practical applications. These heterostructures not only overcome the inherent limitations of individual materials but also enable the realization of new properties through appropriate combinations, establishing a platform to explore new physical and chemical properties at micro-nano-pico scales. In this review, we systematically summarize the latest research progress in the synthesis, modulation, and application of 2D TMD heterostructures. We first introduce the latest techniques for fabricating 2D TMD heterostructures, examining the rationale, mechanisms, advantages, and disadvantages of each strategy. Furthermore, we emphasize the importance of characteristic modulation in 2D TMD heterostructures and discuss some approaches to achieve novel functionalities. Then, we summarize the representative applications of 2D TMD heterostructures. Finally, we highlight the challenges and future perspectives in the synthesis and device fabrication of 2D TMD heterostructures and provide some feasible solutions.

Multimodal Artificial Synapses for Neuromorphic Application

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

Versatile Cu2ZnSnS4-based synaptic memristor for multi-field-regulated neuromorphic applications

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (-0.39 ± 0.08 V), concentrated Set/Reset voltage distribution (<0.08/0.15 V), and long retention time (>104 s) but also continuously modulable conductance by both electric (different width/interval/amplitude) and light (470-808 nm with different intensity) stimulus. These advantages make the device good electrically and optically simulated synaptic functions, including excitatory and inhibitory, paired-pulsed facilitation, short-/long-term plasticity, spike-timing-dependent plasticity, and "memory-forgetting" behavior. Significantly, decimal arithmetic calculation (addition, subtraction, and commutative law) is realized based on the linear conductance regulation, and high precision pattern recognition (>88%) is well achieved with an artificial neural network constructed by 5 × 5 × 4 memristor array. Predictably, such kesterite-based optoelectronic memristors can greatly open the possibility of realizing multi-functional neuromorphic systems.

Recent progress in emerging two-dimensional organic-inorganic van der Waals heterojunctions

Two-dimensional (2D) materials have attracted significant attention in recent decades due to their exceptional optoelectronic properties. Among them, to meet the growing demand for multifunctional applications, 2D organic-inorganic van der Waals (vdW) heterojunctions have become increasingly popular in the development of optoelectronic devices. These heterojunctions demonstrate impressive capability to synergistically combine the favourable characteristics of organic and inorganic materials, thereby offering a wide range of advantages. Also, they enable the creation of innovative device structures and introduce novel functionalities in existing 2D materials, avoiding the need for lattice matching in different material systems. Presently, researchers are actively working on improving the performance of devices based on 2D organic-inorganic vdW heterojunctions by focusing on enhancing the quality of 2D materials, precise stacking methods, energy band regulation, and material selection. Therefore, this review presents a thorough examination of the emerging 2D organic-inorganic vdW heterojunctions, including their classification, fabrication, and corresponding devices. Additionally, this review offers profound and comprehensive insight into the challenges in this field to inspire future research directions. It is expected to propel researchers to harness the extraordinary capabilities of 2D organic-inorganic vdW heterojunctions for a wider range of applications by further advancing the understanding of their fundamental properties, expanding the range of available materials, and exploring novel device architectures. The ongoing research and development in this field hold potential to unlock captivating advancements and foster practical applications across diverse industries.

Multistate Compound Magnetic Tunnel Junction Synapses for Digital Recognition

The quest to mimic the multistate synapses for bioinspired computing has triggered nascent research that leverages the well-established magnetic tunnel junction (MTJ) technology. Early works on the spin transfer torque MTJ-based artificial neural network (ANN) are susceptible to poor thermal reliability, high latency, and high critical current densities. Meanwhile, work on spin-orbit torque (SOT) MTJ-based ANN mainly utilized domain wall motion, which yields negligibly small readout signals differentiating consecutive states and has designs that are incompatible with technological scale-up. Here, we propose a multistate device concept built upon a compound MTJ consisting of multiple SOT-MTJs (number of MTJs, n = 1-4) on a shared write channel, mimicking the spin-based ANN. The n + 1 resistance states representing varying synaptic weights can be tuned by varying the voltage pulses (±1.5-1.8 V), pulse duration (100-300 ns), and applied in-plane fields (5.5-10.5 mT). A large TMR difference of more than 13.6% is observed between two consecutive states for the 4-cell compound MTJ, a 4-fold improvement from reported state-of-the-art spin-based synaptic devices. The ANN built upon the compound MTJ shows high learning accuracy for digital recognition tasks with incremental states and retraining, achieving test accuracy as high as 95.75% in the 4-cell compound MTJ. These results provide an industry-compatible platform to integrate these multistate SOT-MTJ synapses directly into neuromorphic architecture for in-memory and unconventional computing applications.

High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors

The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3  S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Enhanced electrical and magnetic properties of (Co, Yb) co-doped ZnO memristor for neuromorphic computing

We investigate the morphological, electrical, magnetic, and resistive switching properties of (Co, Yb) co-ZnO for neuromorphic computing. By using hydrothermal synthesized nanoparticles and their corresponding sputtering target, we introduce Co and Yb into the ZnO structure, leading to increased oxygen vacancies and grain volume, indicating grain growth. This growth reduces grain boundaries, enhancing electrical conductivity and room-temperature ferromagnetism in Co and Yb-doped ZnO nanoparticles. We present a sputter-grown memristor with a (Co, Yb) co-ZnO layer between Au electrodes. Characterization confirms the ZnO layer's presence and 100 nm-thick Au electrodes. The memristor exhibits repeatable analog resistance switching, allowing manipulation of conductance between low and high resistance states. Statistical endurance tests show stable resistive switching with minimal dispersion over 100 pulse cycles at room temperature. Retention properties of the current states are maintained for up to 1000 seconds, demonstrating excellent thermal stability. A physical model explains the switching mechanism, involving Au ion migration during "set" and filament disruption during "reset." Current-voltage curves suggest space-charge limited current, emphasizing conductive filament formation. All these results shows good electronic devices and systems towards neuromorphic computing.

Optically Addressing Exciton Spin and Pseudospin in Nanomaterials for Spintronics Applications

Oriented exciton spins that can be generated and manipulated optically are of interest for a range of applications, including spintronics, quantum information science, and neuromorphic computing architectures. Although materials that host such excitons often lack practical coherence times for use on their own, strategic transduction of the magnetic information across interfaces can combine fast modulation with longer-term storage and readout. Several nanostructure systems have been put forward due to their interesting magneto-optical properties and their possible manipulation using circularly polarized light. These material systems are presented here, namely two-dimensional (2D) systems due to the unique spin-valley coupling properties and quantum dots for their exciton fine structure. 2D magnets are also discussed for their anisotropic spin behavior and extensive 2D magnetic states that are not yet fully understood but could pave the way for emergent techniques of magnetic control. This review also details the experimental and theoretical tools to measure and understand these systems along with a discussion on the progress of optical manipulation of spins and magnetic order transitions.

Dissecting the Interplay between Organic Charge-Modulated Field-Effect Transistors and Field-Effect Transistors through Interface Control Engineering

Organic charge-modulated field-effect transistors (OCMFETs) have garnered significant interest as sensing platforms for diverse applications that include biomaterials and chemical sensors owing to their distinct operational principles. This study aims to improve the understanding of driving mechanisms in OCMFETs and optimize their device performance by investigating the correlation between organic field-effect transistors (OFETs) and OCMFETs. By introducing self-assembled monolayers (SAMs) with different functional groups on the AlOx gate dielectric surface, we explored the impact of the surface characteristics on the electrical behavior of both devices. Our results indicate that the dipole moment of the dielectric surface is a critical control variable in the performance correlation between OFET and OCMFET devices, as it directly impacts the generation of the induced floating gate voltage through the control gate voltage. The insights obtained from this study contribute to the understanding of the factors affecting OCMFET performance and emphasize their potential as platforms for diverse sensing systems.

Donor-Acceptor Copolymers with 9-(2-Ethylhexyl)carbazole or Dibenzothiophene-5,5-dioxide Donor Units and 5,6-Difluorobenzo[c][1,2,5]thiadiazole Acceptor Units for Photonics

Semiconducting polymers, particularly of the third generation, including donor-acceptor (D-A) copolymers, are extensively studied due to their huge potential for photonic and electronic applications. Here, we report on two new D-A copolymers, CP1 and CP2, composed of different electron-donor (D) units: 9-(2-ethylhexyl)carbazole or dibenzothiophene-5,5-dioxide, respectively, and of 4,7-bis(4'-(2-octyldodecyl)thiophen-2'-yl)-5,6-difluorobenzo[c][1,2,5]thiadiazole building block with central 5,6-difluorobenzo[c][1,2,5]thiadiazole electron-acceptor (A) units, which were synthesized by Suzuki coupling in the high-boiling solvent xylene and characterized. The copolymers exhibited very good thermal and oxidation stability. A copolymer CP1 with different molecular weights was prepared in order to facilitate a comparison of CP1 with CP2 of comparable molecular weight and to reveal the relationship between molecular weight and properties. The photophysical, electrochemical, and electroluminescence properties were examined. Intense red photoluminescence (PL) with higher PL efficiencies for CP1 than for CP2 was observed in both solutions and films. Red shifts in the PL thin film spectra compared with the PL solution spectra indicated aggregate formation in the solid state. X-ray diffraction measurements revealed differences in the arrangement of molecules in thin films depending on the molecular weight of the copolymers. Light-emitting devices with efficient red emission and low onset voltages were prepared and characterized.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Carbazole-Fluorene Copolymers with Various Substituents at the Carbazole Nitrogen: Structure-Properties Relationship

Carbazole derivatives, carbazole-containing polymers and iridium complexes are of interest due to many possible applications in photonics, electronics and biology, particularly as active or hole-transporting layers in organic as well as perovskite devices due to their interesting properties. Here, a series of carbazole-fluorene conjugated copolymers with various substituents at the N-carbazole position (2-methoxycarbonylethyl, 2-carboxyethyl, 2-ethylhexyl, and nonan-2,4-dionatoiridium(III)bis(2-phenylpyridine-N,C^2')-9-yl) was prepared by Suzuki coupling. Their photophysical, electrochemical and electroluminescence (EL) properties were studied. Effects of molecular weight and substituents attached to carbazole unit on their properties are reported. The carbazole-fluorene copolymers in dilute solutions exhibited intense photoluminescence (PL) emission in the blue spectral region with high PL quantum yields (78-87%) except for the copolymer with the iridium complex (23%). Similar PL spectra were observed in dilute solutions. More pronounced differences were found in thin film PL and EL properties due to excimer/aggregate formation. Light-emitting devices (LEDs) made of copolymers with 2-ethylhexyl as N-carbazole substituent exhibited efficient EL emission with the best performance and the lowest EL onset voltages (3-4 V), while the LEDs made of copolymers with other substituents were not as efficient, but showed anomalous behavior and memory effects in current-voltage characteristics promising also for bio-inspired electronics.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

2D Heterostructures for Ubiquitous Electronics and Optoelectronics: Principles, Opportunities, and Challenges

A grand family of two-dimensional (2D) materials and their heterostructures have been discovered through the extensive experimental and theoretical efforts of chemists, material scientists, physicists, and technologists. These pioneering works contribute to realizing the fundamental platforms to explore and analyze new physical/chemical properties and technological phenomena at the micro-nano-pico scales. Engineering 2D van der Waals (vdW) materials and their heterostructures via chemical and physical methods with a suitable choice of stacking order, thickness, and interlayer interactions enable exotic carrier dynamics, showing potential in high-frequency electronics, broadband optoelectronics, low-power neuromorphic computing, and ubiquitous electronics. This comprehensive review addresses recent advances in terms of representative 2D materials, the general fabrication methods, and characterization techniques and the vital role of the physical parameters affecting the quality of 2D heterostructures. The main emphasis is on 2D heterostructures and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical responses in the optical, valley, and topological states. Finally, we discuss the universality of 2D heterostructures with representative applications and trends for future electronics and optoelectronics (FEO) under the challenges and opportunities from physical, nanotechnological, and material synthesis perspectives.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

Bird-Inspired Self-Navigating Artificial Synaptic Compass

Extrasensory neuromorphic devices that can recognize, memorize, and learn stimuli imperceptible to human beings are of considerable interest in interactive intelligent electronics research. This study presents an artificially intelligent magnetoreceptive synapse inspired by the magnetocognitive ability used by birds for navigation and orientation. The proposed synaptic platform is based on arrays of ferroelectric field-effect transistors with air-suspended magneto-interactive top-gates. A suspended gate of an elastomeric composite with superparamagnetic particles laminated with an electrically conductive polymer is mechanically deformed under a magnetic field, facilitating control of the magnetic-field-dependent contact area of the suspended gate with an underlying ferroelectric layer. The remanent polarization of the ferroelectric layer is electrically programmed with the deformed suspended gate, resulting in analog conductance modulation as a function of the magnitude, number, and time interval of the input magnetic pulses. The proposed extrasensory magnetoreceptive synapse may be used as an artificially intelligent synaptic compass that facilitates barrier-adaptable navigation and mapping of a moving object.

Advanced Organic Transistor-Based Sensors Utilizing a Solvatochromic Medium with Twisted Intramolecular Charge-Transfer Behavior and Its Application to Ammonia Gas Detection

Here, we designed and developed an organic field-effect transistor (OFET)-based gas sensor by applying solvatochromic dye (Nile red, NR) with twisted intramolecular charge-transfer (TICT) behavior depending on the polarity of the surrounding molecules, as an auxiliary NR sensing medium (aNR-SM). As a polar molecule approaches, intra-charge transfers from the donor diethylamine group to the ketone group occur in the NR molecule, resulting in the twisting of the donor functional group and thereby increasing its dipole moment. Using this characteristic, NR was applied as an auxiliary sensing medium to the OFET for detecting ammonia (NH₃), a representative toxic gas. The Top-NR case, where the aNR-SM covers only the top of the organic semiconductor layer, showed the best gas sensing performance, and its response and recovery rates were improved by 46 and 94%, respectively, compared to the pristine case. More importantly, a sensitivity of 0.87 ± 0.045 ppm^-1 % was measured, having almost perfect linearity (0.999) over the range of measured NH₃ concentrations, which is the result of solving the saturation problem in the sensing characteristics of the OFET-based gas sensor. Our result not only improved the sensing performance of the OFET-based sensor but also made an important advance in that the reliability of the sensing performance was easily secured by applying solvatochromic and TICT behaviors of an auxiliary sensing medium.

Controlling Native Oxidation of HfS2 for 2D Materials Based Flash Memory and Artificial Synapse

Two-dimensional (2D) materials based artificial synapses are important building blocks for the brain-inspired computing systems that are promising in handling large amounts of informational data with high energy-efficiency in the future. However, 2D devices usually rely on deposited or transferred insulators as the dielectric layer, resulting in various challenges in device compatibility and fabrication complexity. Here, we demonstrate a controllable and reliable oxidation process to turn 2D semiconductor HfS₂ into native oxide, HfO_x, which shows good insulating property and clean interface with HfS₂. We then incorporate the HfO_x/HfS₂ heterostructure into a flash memory device, achieving a high on/off current ratio of ∼10⁵, a large memory window over 60 V, good endurance, and a long retention time over 10³ seconds. In particular, the memory device can work as an artificial synapse to emulate basic synaptic functions and feature good linearity and symmetry in conductance change during long-term potentiation/depression processes. A simulated artificial neural network based on our synaptic device achieves a high accuracy of ∼88% in MNIST pattern recognition. Our work provides a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.