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

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

One-Selector-One-Resistor Integrated Memory Cells Based on Two-Dimensional Heterojunction Memory Selectors

Selectors are critical components for reducing the sneak path leakage currents in emerging resistive random-access memory (RRAM) arrays. Two-dimensional (2D) materials provide a rich choice of materials with van der Waals stacking to form the heterostructure selectors with controllable energy barriers. Here, we experimentally demonstrate 2D-material-based heterostructure selectors with exponential current-voltage (I-V) relationships and integrate them with hafnium oxide (HfOx)-based RRAMs, forming one-selector-one-resistor (1S1R) cells. The multilayer graphene (MG)/tungsten disulfide (WS2)/platinum (Pt) selector contains two asymmetric heterojunctions with different Schottky barriers, which lead to highly nonlinear and asymmetric I-V characteristics. The 2D selectors in 1S1R cells can successfully drive RRAMs, reduce sneak path leakage current by more than 100 times, and provide the set compliance current. The 1S1R cells are further modeled and integrated into both planar and 3D memory arrays, with circuit-level simulations demonstrating that the presence of 2D selectors in large memory arrays can reduce the power consumption by up to 86%, improve the read/write margin by up to 31%, and avoid write failure. Such a platform holds high potential for constructing 3D high-density memories and performing in-memory computing.

Synaptic Transistors Using Scalable Graphene Nanoribbons

Graphene has demonstrated potential for use in neuromorphic electronics due to its superior electrical properties. However, these devices are all based on graphene sheets without patterning, restricting its applications. Here, we demonstrate a graphene nanoribbon synaptic transistor (GNST), with the graphene nanoribbon (GNR) channels fabricated using an electro-hydrodynamically printed nanowire array as lithographic masks for scalable fabrication. The GNST shows tunable synaptic plasticity by spike duration, frequency, and number. Moreover, the device is energy-efficient and ambipolar and shows a regulated response by nanoribbon width. The characteristics of GNSTs are applicable to pattern recognition, showing an accuracy of 84.5%. The device is applicable to Pavlov's classical conditioning. This study reports the first synaptic transistor based on GNRs, providing new insights into future neuromorphic electronics.

Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications

Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks. In this study, the neuromorphic electrical characteristics of organic synaptic electrolyte-gated transistors are correlated with the microstructural and interfacial properties of the active layers. This is accomplished by utilizing a semiconducting/insulating polyblend-based pseudobilayer with embedded source and drain electrodes, referred to as PB-ESD architecture. Three variations of poly(3-hexylthiophene) (P3HT)/poly(methyl methacrylate) (PMMA) PB-ESD-based organic synaptic transistors are fabricated, each exhibiting distinct microstructures and electrical characteristics, thus serving excellent samples for exploring the critical factors influencing neuro-electrical properties. Poor microstructures of P3HT within the active layer and a flat active layer/ion-gel interface correspond to typical neuromorphic behaviors such as potentiated excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-term potentiation (STP). Conversely, superior microstructures of P3HT and a rough active layer/ion-gel interface correspond to significantly higher channel conductance and enhanced EPSC and PPF characteristics as well as long-term potentiation behavior. Such devices were further applied to the simulation of neural networks, which produced a good recognition accuracy. However, excessive PMMA penetration into the P3HT conducting channel leads to features of a depressed EPSC and paired-pulse depression, which are uncommon in organic synaptic transistors. The inclusion of a second gate electrode enables the as-prepared organic synaptic transistors to function as two-input synaptic logic gates, performing various logical operations and effectively mimicking neural modulation functions. Microstructure and interface engineering is an effective method to modulate the neuromorphic behavior of organic synaptic transistors and advance the development of bionic artificial neural networks.

In Situ Growth of Wafer-Scale Patterned Graphene and Fabrication of Optoelectronic Artificial Synaptic Device Array Based on Graphene/n-AlGaN Heterojunction for Visual Learning

The unique optical and electrical properties of graphene-based heterojunctions make them significant for artificial synaptic devices, promoting the advancement of biomimetic vision systems. However, mass production and integration of device arrays are necessary for visual imaging, which is still challenging due to the difficulty in direct growth of wafer-scale graphene patterns. Here, a novel strategy is proposed using photosensitive polymer as a solid carbon source for in situ growth of patterned graphene on diverse substrates. The growth mechanism during high-temperature annealing is elucidated, leading to wafer-scale graphene patterns with exceptional uniformity, ideal crystalline quality, and precise control over layer number by eliminating the release of volatile from oxygen-containing resin. The growth strategy enables the fabrication of two-inch optoelectronic artificial synaptic device array based on graphene/n-AlGaN heterojunction, which emulates key functionalities of biological synapses, including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity. Moreover, the mimicry of visual learning in the human brain is attributed to the regulation of excitatory and inhibitory post-synapse currents, following a learning rule that prioritizes initial recognition before memory formation. The duration of long-term memory reaches 10 min. The in situ growth strategy for patterned graphene represents the novelty for fabricating fundamental hardware of an artificial neuromorphic system.

A Zero-Voltage-Writing Artificial Nervous System Based on Biosensor Integrated on Ferroelectric Tunnel Junction

The artificial nervous system proves the great potential for the emulation of complex neural signal transduction. However, a more bionic system design for bio-signal transduction still lags behind that of physical signals, and relies on additional external sources. Here, this work presents a zero-voltage-writing artificial nervous system (ZANS) that integrates a bio-source-sensing device (BSSD) for ion-based sensing and power generation with a hafnium-zirconium oxide-ferroelectric tunnel junction (HZO-FTJ) for the continuously adjustable resistance state. The BSSD can use ion bio-source as both perception and energy source, and then output voltage signals varied with the change of ion concentrations to the HZO-FTJ, which completes the zero-voltage-writing neuromorphic bio-signal modulation. In view of in/ex vivo biocompatibility, this work shows the precise muscle control of a rabbit leg by integrating the ZANS with a flexible nerve stimulation electrode. The independence on external source enhances the application potential of ZANS in robotics and prosthetics.

Optoelectronic Synapse Behaviors in Tb3+ and Al3+ Co-Doped CaSnO3 with Long-Persistent Luminescence

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo-synaptic behavior in the field of rare earth ion-doped luminescent oxides by using long-persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic-based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV-light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired-pulse facilitation, pulse-number/timing dependent potentiation, and pulse-number/timing dependent short- to long-term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli.

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Reconfigurable Neuromorphic Computing with 2D Material Heterostructures for Versatile Neural Information Processing

Reconfigurable neuromorphic computing holds promise for advancing energy-efficient neural network implementation and functional versatility. Previous work has focused on emulating specific neural functions rather than an integrated approach. We propose an all two-dimensional (2D) material-based heterostructure capable of performing multiple neuromorphic operations by reconfiguring output terminals in response to stimuli. Specifically, our device can synergistically emulate the key neural elements of the synapse, neuron, and dendrite, which play important and interrelated roles in information processing. Dendrites, the branches that receive and transmit presynaptic action potentials, possess the ability to nonlinearly integrate and filter incoming signals. The proposed heterostructure allows reconfiguration between different operation modes, demonstrating its potential for diverse computing tasks. As a proof of concept, we show that the device can perform basic Boolean logic functions. This highlights its applicability to complex neural-network-based information processing problems. Our integrated neuromorphic approach may advance the development of versatile, low-power neuromorphic hardware.

Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors

Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

Light-Intensity Switching of Graphene/WSe2 Synaptic Devices

2D van der Waals heterojunctions (vdWH) have emerged as an attractive platform for the realization of optoelectronic synaptic devices, which are critical for energy-efficient computing systems. Photogating induced by charge traps at the interfaces indeed results in ultrahigh responsivity and tunable photoconductance. Yet, optical potentiation and depression remain mostly modulated by gate bias, requiring relatively high energy inputs. Thus, advanced all-optical synapse switching strategies are still needed. In this work, a reversible switching between positive photoconductivity (PPC) and negative photoconductivity (NPC) is achieved in graphene/WSe2 vdWH solely through light-intensity modulation. Consequently, the graphene/WSe2 synaptic device shows tunable optical potentiation and depression behavior with an ultralow power consumption of 127 aJ. The study further unravels the complex interplay of gate bias and incident light power in determining the sign and magnitude of the photocurrent, showing the critical role of charge trapping and photogating at interfaces. Interestingly, it is found that switching between PPC to NPC can be also obtained at 0 mV drain-source voltage. Overall, the reversible potentiation/depression effect based on light intensity modulation and its combination with additional gate bias tunability is very appealing for the development of energy-efficient optical communications and neuromorphic computing.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Photo-Assisted Ferroelectric Domain Control for α-In2Se3 Artificial Synapses Inspired by Spontaneous Internal Electric Fields

α-In2Se3 semiconductor crystals realize artificial synapses by tuning in-plane and out-of-plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α-In2Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α-In2Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α-In2Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α-In2Se3/hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built-in electric field on synapse characterization. The biased dipoles in α-In2Se3 toward potentiation polarization direction by an enhanced internal built-in electric field under illumination of white light lead to improvement of linearity for long-term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α-In2Se3/hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand-written digit number images.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Pseudocapacitance-Induced Synaptic Plasticity of Tribo-Phototronic Effect Between Ionic Liquid and 2D MoS2

Contact-induced electrification, commonly referred to as triboelectrification, is the subject of extensive investigation at liquid-solid interfaces due to its wide range of applications in electrochemistry, energy harvesting, and sensors. This study examines the triboelectric between an ionic liquid and 2D MoS2 under light illumination. Notably, when a liquid droplet slides across the MoS2 surface, an increase in the generated current and voltage is observed in the forward direction, while a decrease is observed in the reverse direction. This suggests a memory-like tribo-phototronic effect between ionic liquid and 2D MoS2 . The underlying mechanism behind this tribo-phototronic synaptic plasticity is proposed to be ion adsorption/desorption processes resulting from pseudocapacitive deionization/ionization at the liquid-MoS2  interface. This explanation is supported by the equivalent electrical circuit modeling, contact angle measurements, and electronic band diagrams. Furthermore, the influence of various factors such as velocity, step size, light wavelength and intensity, ion concentration, and bias voltage is thoroughly investigated. The artificial synaptic plasticity arising from this phenomenon exhibits significant synaptic features, including potentiation/inhibition, paired-pulse facilitation/depression, and short-term memory (STM) to long-term memory (LTM) transition. This research opens up promising avenues for the development of synaptic memory systems and intelligent sensing applications based on liquid-solid interfaces.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.

Nanoscale Probing of Electrical Memory Effects in van der Waals Layered PdSe2

Tunable electronic materials that can be switched between different impedance states are fundamental to the hardware elements for neuromorphic computing architectures. This "brain-like" computing paradigm uses highly paralleled and colocated data processing, leading to greatly improved energy efficiency and performance compared to traditional architectures in which data have to be frequently transferred between processor and memory. In this work, we use scanning microwave impedance microscopy for nanoscale electrical and electronic characterization of two-dimensional layered semiconductor PdSe2 to probe neuromorphic properties. The local resolution of tens of nanometers reveals significant differences in electronic behavior between and within PdSe2 nanosheets (NSs). In particular, we detected both n-type and p-type behaviors, although previous reports only point to ambipolar n-type dominating characteristics. Nanoscale capacitance-voltage curves and subsequent calculation of characteristic maps revealed a hysteretic behavior originating from the creation and erasure of Se vacancies as well as the switching of defect charge states. In addition, stacks consisting of two NSs show enhanced resistive and capacitive switching, which is attributed to trapped charge carriers at the interfaces between the stacked NSs. Stacking n- and p-type NSs results in a combined behavior that allows one to tune electrical characteristics. As local inhomogeneities of electrical and electronic behavior can have a significant impact on the overall device performance, the demonstrated nanoscale characterization and analysis will be applicable to a wide range of semiconducting materials.

Adaptive Neural Activation and Neuromorphic Processing via Drain-Injection Threshold-Switching Float Gate Transistor Memory

Hetero-modulated neural activation is vital for adaptive information processing and learning that occurs in brain. To implement brain-inspired adaptive processing, previously various neurotransistors oriented for synaptic functions are extensively explored, however, the emulation of nonlinear neural activation and hetero-modulated behaviors are not possible due to the lack of threshold switching behavior in a conventional transistor structure. Here, a 2D van der Waals float gate transistor (FGT) that exhibits steep threshold switching behavior, and the emulation of hetero-modulated neuron functions (integrate-and-fire, sigmoid type activation) for adaptive sensory processing, are reported. Unlike conventional FGTs, the threshold switching behavior stems from impact ionization in channel and the coupled charge injection to float gate. When a threshold is met, a sub-30 mV dec-1 increase of transistor conductance by more than four orders is triggered with a typical switch time of approximately milliseconds. Essentially, by feeding light sensing signal as the modulation input, it is demonstrated that two typical tasks that rely on adaptive neural activation, including collision avoidance and adaptive visual perception, can be realized. These results may shed light on the emulation of rich hetero-modulating behaviors in biological neurons and the realization of biomimetic neuromorphic processing at low hardware cost.

Photothermally Activated Artificial Neuromorphic Synapses

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. To incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to "activate" these neurons and store "memory" by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

Graphene-based RRAM devices for neural computing

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.