Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor.

Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.

Spin-Selective Memtransistors with Magnetized Graphene.

Spin-polarized bands in pristine and proximity-induced magnetic materials are promising building blocks for future devices. Conceptually new memory, logic, and neuromorphic devices are conceived based on atomically thin magnetic materials and the manipulation of their spin-polarized bands via electrical and optical methods. A critical remaining issue is the direct probe and the optimized use of the magnetic coupling effect in van der Waals heterostructures, which requires further delicate design of atomically thin magnetic materials and devices. Here, a spin-selective memtransistor with magnetized single-layered graphene on a reactive antiferromagnetic material, CrI3, is reported. The spin-dependent hybridization between graphene and CrI3 atomic layers enables the spin-selective bandgap opening in the single-layered graphene and the electric field control of magnetization in a specific CrI3 layer. The microscopic working principle is clarified by the first-principles calculations and theoretical analysis of the transport data. Reliable memtransistor operations (i.e., memory and logic device-combined operations), as well as a spin-selective probe of Landau levels in the magnetized graphene, are achieved by using the subtle manipulation of the magnetic proximity effect via electrical means.

Integration of MoS2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization.

In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges.

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.

Two-Dimensional Tunneling Memtransistor with Thin-Film Heterostructure for Low-Power Logic-in-Memory Complementary Metal-Oxide Semiconductor.

With the demand for high-performance and miniaturized semiconductor devices continuously rising, the development of innovative tunneling transistors via efficient stacking methods using two-dimensional (2D) building blocks has paramount importance in the electronic industry. Hence, 2D semiconductors with atomically thin geometries hold significant promise for advancements in electronics. In this study, we introduced tunneling memtransistors with a thin-film heterostructure composed of 2D semiconducting MoS2 and WSe2. Devices with the dual function of tuning and memory operation were realized by the gate-regulated modulation of the barrier height at the heterojunction and manipulation of intrinsic defects within the exfoliated nanoflakes using solution processes. Further, our investigation revealed extensive edge defects and four distinct defect types, namely monoselenium vacancies, diselenium vacancies, tungsten vacancies, and tungsten adatoms, in the interior of electrochemically exfoliated WSe2 nanoflakes. Additionally, we constructed complementary metal-oxide semiconductor-based logic-in-memory devices with a small static power in the range of picowatts using the developed tunneling memtransistors, demonstrating a promising approach for next-generation low-power nanoelectronics.

Emerging Opportunities for 2D Materials in Neuromorphic Computing.

Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

Optoelectronic Synaptic Memtransistor Based on 2D SnSe/MoS2 van der Waals Heterostructure under UV-Ozone Treatment.

Memristive switching devices with electrically and optically invoked synaptic behaviors show great promise in constructing an artificial biological visual system. Through rational design and integration, 2D materials and their van der Waals (vdW) heterostructures can be applied to realize multifunctional optoelectronic devices. Here, a multifunctional optoelectronic synaptic memtransistor based on a SnSe/MoS2 vdW p-n heterojunction to simulate the human biological visual system is reported. By employing simple mild UV-ozone treatment, the device exhibits reversible resistive switching (RS) behavior with switching ratio up to 103 . The retina-like selective response to different input light wavelengths is activated, as well as programmable multilevel resistance states and long-term synaptic plasticity. Moreover, memory and logic functions analogous to those found in the visual cortex of the brain are performed by controlling the optical and electrical input signals. This work proposes a feasible strategy to modulate RS in vdW heterostructures for memristive devices, which show significant potential for neuromorphic processing.

Physical Insights into Vacancy-Based Memtransistors: Toward Power Efficiency, Reliable Operation, and Scalability.

Memtransistors that combine the properties of transistor and memristor hold significant promise for in-memory computing. While superior data storage capability is achieved in memtransistors through gate voltage-induced conductance modulation, the lateral device configuration would not only result in high write bias, which compromises the power efficiency, but also suffers from unsuccessful memory reset that leads to reliability concerns. To circumvent such performance limitations, an advanced physics-based model is required to uncover the dynamic resistive switching behavior and deduce the key driving parameters for the switching process. This work demonstrates a self-consistent physics-based model which incorporates the often-overlooked effects of lattice temperature, vacancy dynamics, and channel electrostatics to accurately solve the interaction between gate potential, ions, and carriers on the memristive switching mechanism. The completed model is carefully calibrated with an ambipolar WSe2 memtransistor and hence enables the investigation of the carrier polarity effect (electrons vs holes) on vacancy transport. Nevertheless, the validity of the model can be extended to different materials by a simple material-dependent parameter modification. Building upon the existing understanding of Schottky barrier height modulation, our study reveals three key insights─leveraging threshold voltage shifts to lower write bias; optimizing lattice temperature distribution and read bias polarity to achieve successful memory state recovery; engineering contact work function to overcome the detrimental parasitic current flow in short channel ambipolar memtransistors. Therefore, understanding the significant correlation between the switching mechanisms, different material systems, and device structures allows performance optimization of operating modes and device designs for future memtransistors-based computing systems.

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/ZrO2-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 ZrO2-x layer below the MoS2, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS2/ZrO2-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/ZrO2-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.

Printed Memtransistor Utilizing a Hybrid Perovskite/Organic Heterojunction Channel.

Neuromorphic computing has the potential to address the inherent limitations of conventional integrated circuit technology, ranging from perception, pattern recognition, to memory and decision-making ( Acc. Chem. Res. 2019, 52 (4), 964-974) ( Nature 2004, 431 (7010), 796-803) ( Nat. Nanotechnol. 2013, 8 (1), 13-24). Despite their low power consumption ( Nano Lett. 2016, 16 (11), 6724-6732), traditional two-terminal memristors can perform only a single function while lacking heterosynaptic plasticity ( Nanotechnology 2013, 24 (38), 382001). Inspired by the unconditioned reflex, multiterminal memristive transistors (memtransistor) were developed to realize complex functions, such as multiterminal modulation and heterosynaptic plasticity ( Nature 2018, 554, (7693), 500-504). Here we combine a hybrid metal halide perovskite with an organic conjugated polymer to form heterojunction transistors that are responsive to both electrical and optical stimuli. We show that the synergistic effects of photoinduced ion migration in the perovskite and electronic transport in the polymer layers can be exploited to realize memristive functions. The device combines reversible, nonvolatile conductance modulation with large switching current ratios, high endurance, and long retention times. Using in situ scanning Kelvin probe microscopy and variable-temperature charge transport measurement, we correlate the collective effects of bias-induced and photoinduced ion migration with the heterosynaptic behavior observed in this hybrid memtransistor. The hybrid heterojunction channel concept is expected to be applicable to other material combinations making it a promising platform for deployment in innovative neuromorphic devices of the future.

Artificial Synapse Based on a 2D-SnO2 Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing.

A new type of two-dimensional (2D) SnO2 semiconductor-based gate-tunable memristor, that is, a memtransistor, an integrated device of a memristor and a transistor, was demonstrated to advance next-generation neuromorphic computing technology. The polycrystalline 2D-SnO2 memristors derived from a low-temperature and vacuum-free liquid metal process offer several interesting resistive switching properties such as excellent digital/analog resistive switching, multistate storage, and gate-tunability function of resistance switching states. Significantly, the gate tunability function that is not achievable in conventional two-terminal memristors provides the capability to implement heterosynaptic analog switching by regulating gate bias for enabling complex neuromorphic learning. We successfully demonstrated that the gate-tunable synaptic device dynamically modulated the analog switching behavior with good linearity and an improved conductance change ratio for high recognition accuracy learning. The presented gate-tunable 2D-oxide memtransistor will advance neuromorphic device technology and open up new opportunities to design learning schemes with an extra degree of freedom.

Self-Selective Multi-Terminal Memtransistor Crossbar Array for In-Memory Computing.

Two-terminal resistive switching devices are commonly plagued with longstanding scientific issues including interdevice variability and sneak current that lead to computational errors and high-power consumption. This necessitates the integration of a separate selector in a one-transistor-one-RRAM (1T-1R) configuration to mitigate crosstalk issue, which compromises circuit footprint. Here, we demonstrate a multi-terminal memtransistor crossbar array with increased parallelism in programming via independent gate control, which allows in situ computation at a dense cell size of 3-4.5 F2 and a minimal sneak current of 0.1 nA. Moreover, a low switching energy of 20 fJ/bit is achieved at a voltage of merely 0.42 V. The architecture is capable of performing multiply-and-accumulate operation, a core computing task for pattern classification. A high MNIST recognition accuracy of 96.87% is simulated owing to the linear synaptic plasticity. Such computing paradigm is deemed revolutionary toward enabling data-centric applications in artificial intelligence and Internet-of-things.

Secure Electronics Enabled by Atomically Thin and Photosensitive Two-Dimensional Memtransistors.

The rapid proliferation of security compromised hardware in today's integrated circuit (IC) supply chain poses a global threat to the reliability of communication, computing, and control systems. While there have been significant advancements in detection and avoidance of security breaches, current top-down approaches are mostly inadequate, inefficient, often inconclusive, and resource extensive in time, energy, and cost, offering tremendous scope for innovation in this field. Here, we introduce an energy and area efficient non-von Neumann hardware platform providing comprehensive and bottom-up security solutions by exploiting inherent device-to-device variation, electrical programmability, and persistent photoconductivity demonstrated by atomically thin two-dimensional memtransistors. We realize diverse security primitives including physically unclonable function, anticounterfeit measures, intellectual property (IP) watermarking, and IC camouflaging to prevent false authentication, detect recycled and remarked ICs, protect IP theft, and stop reverse engineering of ICs.

Electric and Light Dual-Gate Tunable MoS2 Memtransistor.

Memtransistor is a multiterminal device combining the concepts of memristor and field-effect transistor with two-dimensional (2D) materials. The gate tunability of resistive switching in 2D memtransistor enables the multifunctional modulation and promising applications in neuromorphic network. However, the tunability of switching ratio in 2D memtransistor remains small and seriously limits its practical application. Here, we investigate a memtransistor based on a 3-layer MoS2 and realize the electric, light, and their combined modulations. In the electric gate mode, switching ratio is tunable in a large scale in the range 100-105. In the light gate mode, a maximum conductance change of 450% can be obtained by increasing the light power. Moreover, the switching ratio can be further improved to ∼106 through a combination of electric and light dual gating. Such a gating effect can be ascribed to the modulation of carrier density in the MoS2 channel. Our work provides a simple approach for achieving a high-performance multifunctional memtransistor.