Oxidation-boosted charge trapping in ultra-sensitive van der Waals materials for artificial synaptic features

Ultralow-pressure mechanical-motion switching of ferroelectric polarization

Ferroelectric polarization switching, achieved by mechanical forces, enables the storage of stress information in ferroelectrics and holds promise for human interface applications. The prevailing mechanical approach is locally induced flexoelectricity with large strain gradients. However, this approach usually requires huge mechanical forces, which greatly impede device applications. Here, we report an approach of using triboelectric effect to mechanically, reversibly switch ferroelectric polarization across α-In2Se3 ferroelectric memristors. Through contact electrification and electrostatic induction effects, triboelectric units are used to sensitively detect mechanical forces and generate electrical voltage pulses to trigger α-In2Se3 resistance switching. We realize multilevel resistance states under different mechanical forces, by which a neuromorphic stress system is demonstrated. Notably, we achieve the reversal of α-In2Se3 ferroelectric polarization with a record-low mechanical force of ~10 kilopascals and even with tactile touches. Our work provides a fundamental but pragmatic strategy for creating mechanical tactile ferroelectric memory devices.

8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks

The fast development of artificial intelligence has called for high-efficiency neuromorphic computing hardware. While two-dimensional floating-gate memories show promise, their limited state numbers and stability hinder practical use. Here, we report gate-injection-mode two-dimensional floating-gate memories as a candidate for large-scale neural network accelerators. Through a coplanar device structure design and a bi-pulse state programming strategy, 8-bit states with intervals larger than three times of the standard deviations and stability over 10,000 s are achieved at 3 V. The cycling endurance is over 105 and the fabricated 256 devices show a yield of 94.9%. Leveraging this, we carry out experimental image convolutions and 38,592 kernels transplanting on an integrated 9 × 2 array that exhibits results matching well with simulations. We also show that fix-point neural networks with 8-bit precision have inference accuracies approaching the ideal values. Our work validates the potential of gate-injection-mode two-dimensional floating-gate memories for high-efficiency neuromorphic computing hardware.

Flexible Temperature Sensor with 2D In2Se3 Ferroelectric-Semiconductor Field Effect Transistor Exhibiting Record High Sensitivity

In this study, a In2Se3 ferroelectric-semiconductor field-effect transistor is reported for highly sensitive temperature sensing. The high thermal sensitivity of 696%/°C can be achieved by integrating semiconducting behavior and ferroelectricity in In2Se3 thin film. By inducing a polarization up state in the ferroelectric-semiconductor by applying gate bias, the carriers within the In2Se3 semiconductor are depleted, suppressing channel formation. Under polarization up state, off-currents are very sensitive to temperature variation, following variable range hopping conduction. The drain currents are found to be in proportional toexp [ T 0 T ] 1 4 $\exp {{[ {\frac{{{{T}_0}}}{{\mathrm{T}}}} ]}^{\frac{1}{4}}}$ with T0 of 1.27 × 1012 K. To achieve variable range hopping transport, defective In2Se3 is deposited at low temperatures (240 °C) using spray pyrolysis. Fabricated In2Se3 temperature sensor on flexible polyimide substrate can detect a broad range of temperatures (RT to 200 °C) with high stability, exhibiting excellent temperature sensing performance. In addition, the very thin flexible temperature sensor array is demonstrated for large area spatial temperature sensing.

Interface Engineering of 2D Materials toward High-Temperature Electronic Devices

High-temperature electronic materials and devices are highly sought after for advanced applications in aerospace, high-speed automobiles, and deep-well drilling, where active or passive cooling mechanisms are either insufficient or impractical. 2D materials (2DMs) represent promising alternatives to traditional silicon and wide-bandgap semiconductors (WBG) for nanoscale electronic devices operating under high-temperature conditions. The development of robust interfaces is essential for ensuring that 2DMs and their devices achieve high performance and maintain stability when subjected to elevated temperatures. This review summarizes recent advancements in the interface engineering of 2DMs for high-temperature electronic devices. Initially, the limitations of conventional silicon-based materials and WBG semiconductors, alongside the advantages offered by 2DMs, are examined. Subsequently, strategies for interface engineering to enhance the stability of 2DMs and the performance of their devices are detailed. Furthermore, various interface-engineered 2D high-temperature devices, including transistors, optoelectronic devices, sensors, memristors, and neuromorphic devices, are reviewed. Finally, a forward-looking perspective on future 2D high-temperature electronics is presented. This review offers valuable insights into emerging 2DMs and their applications in high-temperature environments from both fundamental and practical perspectives.

A Reliable High-Performance Floating-Gate Transistor Based on ZrS2 Native Oxidation for Optoelectronic Synergistic Artificial Synapses

Floating-gate transistors (FGTs), considered the most promising structure among three-terminal van der Waals (vdW) synaptic transistors, possess superiorities in improved data retention, excellent endurance properties, and multibit storage capacity, thereby overcoming the von Neumann bottleneck in conventional computing architectures. However, the dielectric layer in FGT devices typically depends on atomic layer deposition or mechanically transferred insulators, posing several challenges in terms of device compatibility, manufacturing complexity, and performance degradation. Therefore, it is crucial to discover dielectrics compatible with two-dimensional (2D) materials for further simplifying FGT structures and achieving optimal performance. Here, we present a controllable and reliable oxidation process to convert the 2D semiconductor ZrS2 into its native oxide ZrOx and combine ZrOx/ZrS2 with the MoS2 channel to form MoS2/ZrOx/ZrS2 FGT, which exhibits a high on/off ratio of 107, a wide memory window of 101 V, a long retention time of 103 s, a large storage capacity of 7 bits, an excellent PPF index of 269.4%, and low power consumption of 5 pJ. Under photoelectric stimulation, the device stimulates various biological synapse behaviors, including associative memory function and retina-like adaptation. In particular, the device achieves information storage and erasure under solely optical stimulation, exhibiting high consistency with synaptic weight modulation in optogenetics and outstanding optoelectronic storage performance. These results suggest that our work provides a novel and effective approach for simplifying FGT structures and enhancing their performance, holding significant potential for application in next-generation multifunctional memory devices and systems.

In-Plane Polarization-Triggered WS2-Ferroelectric Heterostructured Synaptic Devices

To date, various kinds of memristors have been proposed as artificial neurons and synapses for neuromorphic computing to overcome the so-called von Neumann bottleneck in conventional computing architectures. However, related working principles are mostly ascribed to randomly distributed conductive filaments or traps, which usually lead to high stochasticity and poor uniformity. In this work, a heterostructure with a two-dimensional WS2 monolayer and a ferroelectric PZT film were demonstrated for memristors and artificial synapses, triggered by in-plane ferroelectric polarization. It is noted that the properties of the WS2/PZT heterostructures, including photoluminescence (PL) and conductivity, can be effectively tuned by in-plane polarization. In contrast to conventional memristors, the resistance switch of our memristors relies on the dynamic regulation of Schottky barriers at the WS2/metal contacts by ferroelectric polarization. PL characterizations verified the existence of lateral fields inside the WS2 originating from the polarization of the PZT. In particular, such memristors can emulate neuromorphic functions, including threshold-driven spiking, excitatory postsynaptic current, paired-pulse promotion (PPF), and so on. The results indicate that the WS2/PZT heterostructures with in-plane polarization are promising for the hardware implementation of artificial neural networks.

Two-Dimensional Materials for Brain-Inspired Computing Hardware

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.

Large-scale high uniform optoelectronic synapses array for artificial visual neural network

Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm2, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.

Spatially Precise Light-Activated Dedoping in Wafer-Scale MoS2 Films

2D materials, particularly transition metal dichalcogenides (TMDCs), have shown great potential for microelectronics and optoelectronics. However, a major challenge in commercializing these materials is the inability to control their doping at a wafer scale with high spatial fidelity. Interface chemistry is used with the underlying substrate oxide and concomitant exposure to visible light in ambient conditions for photo-dedoping wafer scale MoS2. It is hypothesized that the oxide layer traps photoexcited holes, leaving behind long-lived electrons that become available for surface reactions with ambient air at sulfur vacancies (defect sites) resulting in dedoping. Additionally, high fidelity spatial control is showcased over the dedoping process, by laser writing, and fine control achieved over the degree of doping by modulating the illumination time and power density. This localized change in MoS2 doping density is very stable (at least 7 days) and robust to processing conditions like high temperature and vacuum. The scalability and ease of implementation of this approach can address one of the major issues preventing the "Lab to Fab" transition of 2D materials and facilitate its seamless integration for commercial applications in multi-logic devices, inverters, and other optoelectronic devices.

2D-SnS-Embedded Schottky Device with Neurotransmitter-Like Functionality Produced Using Proximity Vapor Transfer Method for Photonic Neurocomputing

Neuromorphic computing, which involves the creation of artificial synapses capable of mimicking biological brain activity, has intrigued researchers in the field of artificial intelligence (AI). To advance neuromorphic computing, a highly efficient 2D material-based artificial synapse capable of performing logical and arithmetic operations must be developed. However, fabricating large, uniform films or high-quality structures of 2D materials remains challenging because of their multistep and complex fabrication processes. In the present study, to produce large (Ø ≈ 3 in.), uniform, transparent neuromorphic devices, a novel single-step approach called proximity vapor transfer (PVT) that utilizes van der Waals (vdW) materials is employed. This single-step technique, which involves the fabrication of vdW materials on various substrates (glass, ITO, AZO, Mo, and Cu), allows control of the thickness and bandgap tunability. The Schottky device developed via the PVT method using vdW SnS with neurotransmitter (acetylcholine)-like functionality emulates biological synapses and exhibits photoelectronic synaptic behavior with wide-field-of-view synaptic plasticity. In addition, logic gate operations (NOT, OR, AND), reward-cascade neurotransmission, and imaging can be performed using 3 × 3 arrays of the device. This study represents a significant step toward the development of transparent and large-area synaptic devices, which are crucial for advancing AI applications.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes. The transistors exhibit a large switching ratio of over 105, a significant memory window of ≈12 V arising from charge trapping, and low response delays down to tens of nanoseconds. These device characteristics endow highly stabilized reconfigurable conductance states, successful emulation of synaptic functions, and a high data processing speed. Importantly, the devices exhibit uniform characteristic metrics, e.g., with a 1.8% variation in the memory window, suggesting an industrial-scale manufacturing capability of the fabrication. Using the memories, a hardware convolution kernel is designed and parallel image processing is demonstrated at a speed of 1 M bit per second per input channel. Given the efficacy of the convolution kernel, a promising prospect of the memories in implementing neuromorphic computing is envisaged. To explore the potential, large-scale convolution kernels are simulated and high-speed video processing is realized for autonomous driving.

An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 105, with excellent repeatability. Additionally, a prototype 4 × 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

Two-Dimensional MoS2-Based Anisotropic Synaptic Transistor for Neuromorphic Computing by Localized Electron Beam Irradiation

Neuromorphic computing, a promising solution to the von Neumann bottleneck, is paving the way for the development of next-generation computing and sensing systems. Axon-multisynapse systems enable the execution of sophisticated tasks, making them not only desirable but essential for future applications in this field. Anisotropic materials, which have different properties in different directions, are being used to create artificial synapses that can mimic the functions of biological axon-multisynapse systems. However, the restricted variety and unadjustable conductive ratio limit their applications. Here, it is shown that anisotropic artificial synapses can be achieved on isotropic materials with externally localized doping via electron beam irradiation (EBI) and purposefully induced trap sites. By employing the synapses along different directions, artificial neural networks (ANNs) are constructed to accomplish variable neuromorphic tasks with optimized performance. The localized doping method expands the axon-multisynapse device family, illustrating that this approach has tremendous potentials in next-generation computing and sensing systems.

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

Dual Interactive Mode Human-Machine Interfaces Based on Triboelectric Nanogenerator and IGZO/In2O3 Heterojunction Synaptic Transistor

Imitating human neural networks via bio-inspired electronics advances human-machine interfaces (HMI), overcoming von Neumann limitations and enabling efficient, low-energy data processing in the big data era. However, single-contact mode HMIs have inherent limitations in terms of their capabilities and performances, such as constrained adaptability to dynamic environments, and reduced cognitive processing capabilities. Here, a dual-interactive-mode HMI system based on a triboelectric nanogenerator (TENG) and heterojunction synaptic transistor (HJST) is proposed for both contact and non-contact applications. The TENG incorporates a poly-methyl meth-acrylate (PMMA)-NiCo2S4/S film, in which the NiCo2S4/S composite traps and blocks electrons to optimize charge generation and storage. The heterojunction structure, mitigates the Debye screening effect, thereby improving transistor characteristics and reliability. The integrated TENG-HJST system exhibits synaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and synaptic plasticity, enabling emulation of neural behavior and advanced information processing. Moreover, neural morphology manipulation is demonstrated in practical tasks, such as controlling international chess games. By integrating the TENG-HJST device with a robotic hand, conscious artificial responses are generated, enhancing event accuracy. This breakthrough in dual-interactive-mode interfacing holds promise for HMI systems and neural prostheses.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor

Two-dimensional (2D) transition metal dichalcogenides (TMDs) allow for atomic-scale manipulation, challenging the conventional limitations of semiconductor materials. This capability may overcome the short-channel effect, sparking significant advancements in electronic devices that utilize 2D TMDs. Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance. This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor. It delves into the impacts of miniaturization, including the reduction of channel length, gate length, source/drain contact length, and dielectric thickness on transistor operation and performance. In addition, this review provides a detailed analysis of performance parameters such as source/drain contact resistance, subthreshold swing, hysteresis loop, carrier mobility, on/off ratio, and the development of p-type and single logic transistors. This review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. It also emphasizes the role of 2D TMD-based transistors as memory devices, focusing on enhancing memory operation speed, endurance, data retention, and extinction ratio, as well as reducing energy consumption in memory devices functioning as artificial synapses. This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications. It underscores the anticipated challenges, opportunities, and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Multifunctional In-Memory Logics Based on a Dual-Gate Antiambipolar Transistor toward Non-von Neumann Computing Architecture

In-memory computing may make it possible to realize non-von Neumann computing because the logic circuits are unified in the memory units. We investigated two types of in-memory logic operations, namely, two-input logic circuits and multifunctional artificial synapses. These were realized in a dual-gate antiambipolar transistor (AAT) with a ReS2/WSe2 heterojunction, in which polystyrene with a zinc phthalocyanine core (ZnPc-PS4) was incorporated as a memory layer. Here, reconfigurability is a key concept for both types of device operations and was achieved by merging the Λ-shaped transfer curve of the AAT and the nonvolatile memory effect of ZnPc-PS4. First, we achieved electrically reconfigurable two-input logic circuits. Versatile logic circuits such as AND, OR, NAND, NOR, and XOR circuits were demonstrated by taking advantage of the Λ-shaped transfer curve of the dual-gate AAT. Importantly, the nonvolatile memory function provided the electrical switching of the individual circuits between AND/OR, NAND/NOR, and XOR/NAND circuits with constant input signals. Second, the memory effect was applied to multifunctional artificial synapses. The inhibitory/excitatory and long-term potentiation/depression synaptic operations were electrically reconfigured simply by controlling one parameter (readout voltage), making three distinct responses possible even with the same presynaptic signals. These findings provide hints that may lead to the realization of new in-memory computing architectures beyond the current von Neumann computers.

Tunable Negative and Positive Photoconductance in Van Der Waals Heterostructure for Image Preprocessing

The processing of visual information occurs mainly in the retina, and the retinal preprocessing function greatly improves the transmission quality and efficiency of visual information. The artificial retina system provides a promising path to efficient image processing. Here, graphene/InSe/h-BN heterogeneous structure is proposed, which exhibits negative and positive photoconductance (NPC and PPC) effects by altering the strength of a single wavelength laser. Moreover, a modified theoretical model is presented based on the power-dependent photoconductivity effect of laser:I ph = - mP α 1 + nP α 2 ${\rm I}_{\rm ph}\,=\,-{\rm mP}^{\alpha _{1}} + {\rm nP}^{\alpha _{2}}$ , which can reveal the internal physical mechanism of negative/positive photoconductance effects. The present 2D structure design allows the field effect transistor (FET) to exhibit excellent photoelectric performance (RNPC = 1.1× 104 AW-1, RPPC = 13 AW-1) and performance stability. Especially, the retinal pretreatment process is successfully simulated based on the negative and positive photoconductive effects. Moreover, the pulse signal input improves the device responsivity by 167%, and the transmission quality and efficiency of the visual signal can also be enhanced. This work provides a new design idea and direction for the construction of artificial vision, and lay a foundation for the integration of the next generation of optoelectronic devices.

A synapse with low power consumption based on MoTe2/SnS2heterostructure

The use of two-dimensional materials and van der Waals heterostructures holds great potential for improving the performance of memristors Here, we present SnS2/MoTe2heterostructure synaptic transistors. Benefiting from the ultra-low dark current of the heterojunction, the power consumption of the synapse is only 19pJ per switching under 0.1 V bias, comparable to that of biological synapses. The synaptic device based on the SnS2/MoTe2demonstrates various synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation. In particular, the synaptic weight of the excitatory postsynaptic current can reach 109.8%. In addition, the controllability of the long-term potentiation and long-term depression are discussed. The dynamic range (Gmax/Gmin) and the symmetricity values of the synaptic devices are approximately 16.22 and 6.37, and the non-linearity is 1.79. Our study provides the possibility for the application of 2D material synaptic devices in the field of low-power information storage.

Optoelectronic Synapse Based on 2D Electron Gas in Stoichiometry-Controlled Oxide Heterostructures

Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5 × 5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.

Visualized in-sensor computing

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Ferroelectric 2D SnS2 Analog Synaptic FET

In this study, the development and characterization of 2D ferroelectric field-effect transistor (2D FeFET) devices are presented, utilizing nanoscale ferroelectric HfZrO2 (HZO) and 2D semiconductors. The fabricated device demonstrated multi-level data storage capabilities. It successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). Extensive endurance tests ensured robust stability (107 switching cycles, 105 s (extrapolated to 10 years)), excellent linearity, and high Gmax/Gmin ratio (>105), all of which are essential for realizing multi-level data states (>7-bit operation). Beyond mimicking synaptic functionalities, the device achieved a pattern recognition accuracy of ≈94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset when incorporated into a neural network, demonstrating its potential as an effective component in neuromorphic systems. The successful implementation of the 2D FeFET device paves the way for the development of high-efficiency, ultralow-power neuromorphic hardware which is in sub-femtojoule (48 aJ/spike) and fast response (1 µs), which is 104 folds faster than human synapse (≈10 ms). The results of the research underline the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies.

High-Field Electron Transport and High Saturation Velocity in Multilayer Indium Selenide Transistors

Creating a high-frequency electron system demands a high saturation velocity (υsat). Herein, we report the high-field transport properties of multilayer van der Waals (vdW) indium selenide (InSe). The InSe is on a hexagonal boron nitride substrate and encapsulated by a thin, noncontinuous In layer, resulting in an impressive electron mobility reaching 2600 cm2/(V s) at room temperature. The high-mobility InSe achieves υsat exceeding 2 × 107 cm/s, which is superior to those of other gapped vdW semiconductors, and exhibits a 50-60% improvement in υsat when cooled to 80 K. The temperature dependence of υsat suggests an optical phonon energy (ℏωop) for InSe in the range of 23-27 meV, previously reported values for InSe. It is also notable that the measured υsat values exceed what is expected according to the optical phonon emission model due to weak electron-phonon scattering. The superior υsat of our InSe, despite its relatively small ℏωop, reveals its potential for high-frequency electronics, including applications to control cryogenic quantum computers in close proximity.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

Highly Reliable Van Der Waals Memory Boosted by a Single 2D Charge Trap Medium

Charge trap materials that can store carriers efficiently and controllably are desired for memory applications. 2D materials are promising for highly compacted and reliable memory mainly due to their ease of constructing atomically uniform interfaces, however, remain unexplored as being charge trap media. Here it is discovered that 2D semiconducting PbI2 is an excellent charge trap material for nonvolatile memory and artificial synapses. It is simple to construct PbI2 -based charge trap devices since no complicated synthesis or additional defect manufacturing are required. As a demonstration, MoS2 /PbI2 device exhibits a large memory window of 120 V, fast write speed of 5 µs, high on-off ratio around 106 , multilevel memory of over 8 distinct states, high reliability with endurance up to 104 cycles and retention over 1.2 × 104 s. It is envisioned that PbI2 with ionic activity caused by the natively formed iodine vacancies is unique to combine with unlimited 2D materials for versatile van der Waals devices with high-integration and multifunctionality.

Analog Memory and Synaptic Plasticity in an InGaZnO-Based Memristor by Modifying Intrinsic Oxygen Vacancies

This study focuses on InGaZnO-based synaptic devices fabricated using reactive radiofrequency sputtering deposition with highly uniform and reliable multilevel memory states. Electron trapping and trap generation behaviors were examined based on current compliance adjustments and constant voltage stressing on the ITO/InGaZnO/ITO memristor. Using O2 + N2 plasma treatment resulted in stable and consistent cycle-to-cycle memory switching with an average memory window of ~95.3. Multilevel resistance states ranging from 0.68 to 140.7 kΩ were achieved by controlling the VRESET within the range of -1.4 to -1.8 V. The modulation of synaptic weight for short-term plasticity was simulated by applying voltage pulses with increasing amplitudes after the formation of a weak conductive filament. To emulate several synaptic behaviors in InGaZnO-based memristors, variations in the pulse interval were used for paired-pulse facilitation and pulse frequency-dependent spike rate-dependent plasticity. Long-term potentiation and depression are also observed after strong conductive filaments form at higher current compliance in the switching layer. Hence, the ITO/InGaZnO/ITO memristor holds promise for high-performance synaptic device applications.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Unraveling Spatiotemporal Transient Dynamics at the Nanoscale via Wavelet Transform-Based Kelvin Probe Force Microscopy

Mechanistic probing of surface potential changes arising from dynamic charge transport is the key to understanding and engineering increasingly complex nanoscale materials and devices. Spatiotemporal averaging in conventional heterodyne detection-based Kelvin probe force microscopy (KPFM) inherently limits its time resolution, causing an irretrievable loss of transient response and higher-order harmonics. Addressing this, we report a wavelet transform (WT)-based methodology capable of quantifying the sub-ms charge dynamics and probing the elusive transient response. The feedback-free, open-loop wavelet transform KPFM (OL-WT-KPFM) technique harnesses the WT's ability to simultaneously extract spatial and temporal information from the photodetector signal to provide a dynamic mapping of surface potential, capacitance gradient, and dielectric constant at a temporal resolution 3 orders of magnitude higher than the lock-in time constant. We further demonstrate the method's applicability to explore the surface-photovoltage-induced sub-ms hole-diffusion transient in bismuth oxyiodide semiconductor. The OL-WT-KPFM concept is readily applicable to commercial systems and can provide the underlying basis for the real-time analysis of transient electronic and electrochemical properties.

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering

Novel memory devices are essential for developing low power, fast, and accurate in-memory computing and neuromorphic engineering concepts that can compete with the conventional complementary metal-oxide-semiconductor (CMOS) digital processors. 2D semiconductors provide a novel platform for advanced semiconductors with atomic thickness, low-current operation, and capability of 3D integration. This work presents a charge-trap memory (CTM) device with a MoS2 channel where memory operation arises, thanks to electron trapping/detrapping at interface states. Transistor operation, memory characteristics, and synaptic potentiation/depression for neuromorphic applications are demonstrated. The CTM device shows outstanding linearity of the potentiation by applied drain pulses of equal amplitude. Finally, pattern recognition is demonstrated by reservoir computing where the input pattern is applied as a stimulation of the MoS2 -based CTMs, while the output current after stimulation is processed by a feedforward readout network. The good accuracy, the low current operation, and the robustness to input random bit flip makes the CTM device a promising technology for future high-density neuromorphic computing concepts.

Reinforcing Synaptic Plasticity of Defect-Tolerant States in Alloyed 2D Artificial Transistors

While two-dimensional (2D) materials possess the desirable future of neuromorphic computing platforms, unstable charging and de-trapping processes, which are inherited from uncontrollable states, such as the interface trap between nanocrystals and dielectric layers, can deteriorate the synaptic plasticity in field-effect transistors. Here, we report a facile and effective strategy to promote artificial synaptic devices by providing physical doping in 2D transition-metal dichalcogenide nanomaterials. Our experiments demonstrate that the introduction of niobium (Nb) into 2D WSe2 nanomaterials produces charge trap levels in the band gap and retards the decay of the trapped charges, thereby accelerating the artificial synaptic plasticity by encouraging improved short-/long-term plasticity, increased multilevel states, lower power consumption, and better symmetry and asymmetry ratios. Density functional theory calculations also proved that the addition of Nb to 2D WSe2 generates defect tolerance levels, thereby governing the charging and de-trapping mechanisms of the synaptic devices. Physically doped electronic synapses are expected to be a promising strategy for the development of bioinspired artificial electronic devices.

Single-Gate In-Transistor Readout of Current Superposition and Collapse Utilizing Quantum Tunneling and Ferroelectric Switching

In nanostructure assemblies, the superposition of current paths forms microscopic electric circuits, and different circuit networks produce varying results, particularly when utilized as transistor channels for computing applications. However, the intricate nature of assembly networks and the winding paths of commensurate currents hinder standard circuit modeling. Inspired by the quantum collapse of superposition states for information decoding in quantum circuits, the implementation of analogous current path collapse to facilitate the detection of microscopic circuits by modifying their network topology is explored. Here, the superposition and collapse of current paths in gate-all-around polysilicon nanosheet arrays are demonstrated to enrich the computational resources within transistors by engineering the channel length and quantity. Switching the ferroelectric polarization of Hf0.5 Zr0.5 O2 gate dielectric, which drives these transistors out-of-equilibrium, decodes the output polymorphism through circuit topological modifications. Furthermore, a protocol for the single-electron readout of ferroelectric polarization is presented with tailoring the channel coherence. The introduction of lateral path superposition results into intriguing metal-to-insulator transitions due to transient behavior of ferroelectric switching. This ability to adjust the current networks within transistors and their interaction with ferroelectric polarization in polycrystalline nanostructures lays the groundwork for generating diverse current characteristics as potential physical databases for optimization-based computing.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.

Ultrafast carrier dynamics and layer-dependent carrier recombination rate in InSe

InSe layered semiconductors with high mobility have advantages over transition-metal dichalcogenides in certain device applications. Understanding the dynamics of carriers, especially around the major bandgaps, is not only of fundamental interest but also important for improving the performance of devices. We investigated ultrafast carrier dynamics in exfoliated InSe near the bandgap and found that the presence of photocarriers led to shrinkage in the optical bandgap. In addition, we observed that the carrier recombination rate increased when the thickness of the InSe nanoflakes was reduced and the process was dominated by surface recombination. For the same flakes, the recombination rate became lower after the freshly exfoliated InSe was exposed to air and oxidized. Using a free carrier diffusion model, layer-dependent surface recombination velocities were obtained. Our investigation reveals that the surface condition and the thickness of few-layer InSe play important roles in carrier lifetimes.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.

Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.

Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2-x Heterostructure Memtransistor

Electrically tunable resistive switching of a polycrystalline MoS₂-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS₂ grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS₂/ZrO_2-x heterostructure. Compared to the control device with the MoS₂ semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO_2-x layer below the MoS₂, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS₂/ZrO_2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS₂/ZrO_2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.

Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device

Since the first successful exfoliation of graphene, the superior physical and chemical properties of two-dimensional (2D) materials, such as atomic thickness, strong in-plane bonding energy and weak inter-layer van der Waals (vdW) force have attracted wide attention. Meanwhile, there is a surge of interest in novel physics which is absent in bulk materials. Thus, vertical stacking of 2D materials could be critical to discover such physics and develop novel optoelectronic applications. Although vdW heterostructures have been grown by chemical vapor deposition, the available choices of materials for stacking is limited and the device yield is yet to be improved. Another approach to build vdW heterostructure relies on wet/dry transfer techniques like stacking Lego bricks. Although previous reviews have surveyed various wet transfer techniques, novel dry transfer techniques have been recently been demonstrated, featuring clean and sharp interfaces, which also gets rid of contamination, wrinkles, bubbles formed during wet transfer. This review summarizes the optimized dry transfer methods, which paves the way towards high-quality 2D material heterostructures with optimized interfaces. Such transfer techniques also lead to new physical phenomena while enable novel optoelectronic applications on artificial vdW heterostructures, which are discussed in the last part of this review.

Atomic Layer Deposition of Metal Oxides and Chalcogenides for High Performance Transistors

Atomic layer deposition (ALD) is a deposition technique well-suited to produce high-quality thin film materials at the nanoscale for applications in transistors. This review comprehensively describes the latest developments in ALD of metal oxides (MOs) and chalcogenides with tunable bandgaps, compositions, and nanostructures for the fabrication of high-performance field-effect transistors. By ALD various n-type and p-type MOs, including binary and multinary semiconductors, can be deposited and applied as channel materials, transparent electrodes, or electrode interlayers for improving charge-transport and switching properties of transistors. On the other hand, MO insulators by ALD are applied as dielectrics or protecting/encapsulating layers for enhancing device performance and stability. Metal chalcogenide semiconductors and their heterostructures made by ALD have shown great promise as novel building blocks to fabricate single channel or heterojunction materials in transistors. By correlating the device performance to the structural and chemical properties of the ALD materials, clear structure-property relations can be proposed, which can help to design better-performing transistors. Finally, a brief concluding remark on these ALD materials and devices is presented, with insights into upcoming opportunities and challenges for future electronics and integrated applications.

Asymmetric Ferroelectric-Gated Two-Dimensional Transistor Integrating Self-Rectifying Photoelectric Memory and Artificial Synapse

Ferroelectric field-effect transistors (Fe-FET) are promising candidates for future information devices. However, they suffer from low endurance and short retention time, which retards the application of processing memory in the same physical processes. Here, inspired by the ferroelectric proximity effects, we design a reconfigurable two-dimensional (2D) MoS₂ transistor featuring with asymmetric ferroelectric gate, exhibiting high memory and logic ability with a program/erase ratio of over 10⁶ and a self-rectifying ratio of 10³. Interestingly, the robust electric and optic cycling are obtained with a large switching ratio of 10⁶ and nine distinct resistance states upon optical excitation with excellent nonvolatile characteristics. Meanwhile, the operation of memory mimics the synapse behavior in response to light spikes with different intensity and number. This design realizes an integration of robust processing memory in one single device, which demonstrates a considerable potential of an asymmetric ferroelectric gate in the development of Fe-FETs for logic processing and nonvolatile memory applications.

In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition accuracy

The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate indium gallium zinc oxide (IGZO) synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.

Dual-Ferroelectric-Coupling-Engineered Two-Dimensional Transistors for Multifunctional In-Memory Computing

In-memory computing featuring a radical departure from the von Neumann architecture is promising to substantially reduce the energy and time consumption for data-intensive computation. With the increasing challenges facing silicon complementary metal-oxide-semiconductor (CMOS) technology, developing in-memory computing hardware would require a different platform to deliver significantly enhanced functionalities at the material and device level. Here, we explore a dual-gate two-dimensional ferroelectric field-effect transistor (2D FeFET) as a basic device to form both nonvolatile logic gates and artificial synapses, addressing in-memory computing simultaneously in digital and analog spaces. Through diversifying the electrostatic behaviors in 2D transistors with the dual-ferroelectric-coupling effect, rich logic functionalities including linear (AND, OR) and nonlinear (XNOR) gates were obtained in unipolar (MoS₂) and ambipolar (MoTe₂) FeFETs. Combining both types of 2D FeFETs in a heterogeneous platform, an important computation circuit, i.e., a half-adder, was successfully constructed with an area-efficient two-transistor structure. Furthermore, with the same device structure, several key synaptic functions are shown at the device level, and an artificial neural network is simulated at the system level, manifesting its potential for neuromorphic computing. These findings highlight the prospects of dual-gate 2D FeFETs for the development of multifunctional in-memory computing hardware capable of both digital and analog computation.

ZnO nanowire optoelectronic synapse for neuromorphic computing

Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O₂desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.

Synaptic Plasticity Powering Long-Afterglow Organic Light-Emitting Transistors

Long-lasting luminescence in optoelectronic devices is highly sought after for applications in optical data storage and display technology. While in light-emitting diodes this is achieved by exploiting long-afterglow organic materials as active components, such a strategy has never been pursued in light-emitting transistors, which are still rather unexplored and whose technological potential is yet to be demonstrated. Herein, the fabrication of long-afterglow organic light-emitting transistors (LAOLETs) is reported whose operation relies on an unprecedented strategy based on a photoinduced synaptic effect in an inorganic indium-gallium-zinc-oxide (IGZO) semiconducting channel layer, to power a persistent electroluminescence in organic light-emitting materials. Oxygen vacancies in the IGZO layer, produced by irradiation at λ = 312 nm, free electrons in excess yielding to a channel conductance increase. Due to the slow recombination kinetics of photogenerated electrons to oxygen vacancies in the channel layer, the organic material can be fueled by postsynaptic current and displays a long-lived light-emission (hundreds of seconds) after ceasing UV irradiation. As a proof-of-concept, the LAOLETs are integrated in active-matrix light-emitting arrays operating as visual UV sensors capable of long-lifetime green-light emission in the irradiated regions.

Emerging 2D Memory Devices for In-Memory Computing

It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonvolatile memory unit, has been intensively studied. Among various candidate materials, 2D layered materials have recently demonstrated many new features that have been uniquely exploited to build next-generation electronics. Here, the recent progress of 2D memory devices is reviewed for in-memory computing. For each memory configuration, their operation mechanisms and memory characteristics are described, and their pros and cons are weighed. Subsequently, their versatile applications for in-memory computing technology, including logic operations, electronic synapses, and random number generation are presented. Finally, the current challenges and potential strategies for future 2D in-memory computing systems are also discussed at the material, device, circuit, and architecture levels. It is hoped that this manuscript could give a comprehensive review of 2D memory devices and their applications in in-memory computing, and be helpful for this exciting research area.

Hysteresis-reversible MoS2 transistor

Sulfur vacancy dominant hysteresis in MoS₂ transistors is observed. By decorating with Pt, the hysteresis behavior could switch from sulfur vacancy dominant to interfacial dominant, thereby realizing a hysteresis-reversible MoS₂ transistor.

Tunable and nonvolatile multibit data storage memory based on MoTe2/boron nitride/graphene heterostructures through contact engineering

Heterostructures formed by stacking atomically thin two-dimensional materials are promising candidates for flash memory devices to achieve premium performances, due to the capability of effective carrier modulation and unique charge trapping behavior at the interfaces with atomic flatness. Here, we report a nonvolatile floating-gate flash memory based on MoTe₂/h-BN/graphene van der Waals heterostructure, which possesses increased data storage capacity per cell and versatile tunability. The decent memory behavior of the device is enabled by the carriers stored in the floating gate of graphene layer, which tunnel through the dielectric layer of h-BN from the channel layer of MoTe₂ under static-electrical field. Consequently, the developed memory device is capable to store 2 bits per cell by applying varied gate bias to implement multi-distinctive current levels. The device also exhibits remarkable erase/program current ratio of ∼10⁵ with 1 µs switch speed and stable retention with estimated ∼30% charge loss after 10 yr. Furthermore, the memory device can operate in both p- and n-type modes through contact engineering, offering wide adaptability for emerging applications in electronic technologies, such as neuromorphic computing, data-adaptive energy efficient memory, and complex digital circuits.

Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires

Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi-two-dimensional electron gases (quasi-2DEGs) in InGaO₃(ZnO)₃ superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca²⁺ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.