Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics

Polymorphic phases in 2D In2Se3: fundamental properties, phase transition modulation methodologies and advanced applications

Two-dimensional (2D) In2Se3, which is a multifunctional semiconductor, exhibits multiple crystallographic phases, each of which possesses distinct electronic, optical, and thermal properties. This inherent phase variability makes it a promising candidate for a wide range of applications, including memory devices, photovoltaics, and photodetectors. This review comprehensively explores the latest progress of various polymorphic phases of 2D In2Se3, emphasizing their unique properties, characterization methods, phase modulation strategies, and practical applications. Commencing with a rigorous examination of the structural attributes inherent in its various phases, we introduce sophisticated techniques for its characterization. Subsequently, modulation strategies, encompassing variations in temperature, application of electric fields, induced stress, and alterations in pressure, are explored, each exerting an influence on the phase transitions in 2D In2Se3. Finally, we highlight recent advancements and applications resulting from these phase transitions, including homoepitaxial heterophase structures, optical modulators, and phase change memory (PCM). By synthesizing insights into phase properties, modulation strategies, and potential applications, this review endeavours to provide a comprehensive understanding of the significance and prospects of In2Se3 in the semiconductor field.

Oxygen Vacancy Induced 2D Bi2SeO5 Non-Volatile Memristor for 1T1R Integration

Two-dimensional (2D) layered Bi2SeO5, a novel high-k oxide material, has shown considerable potential for enhancing memristor performance. In this study, high-crystallinity 2D Bi2SeO5 nanosheets were successfully exfoliated, demonstrating that oxygen-vacancy-induced Bi2SeO5 memristors exhibit superior nonvolatile characteristics. Specifically, these memristors exhibit an ultrahigh on/off ratio (up to 1010), an extremely low off-state current (10-12 A), and rapid switching speeds (160 ns for SET and 110 ns for RESET). Moreover, the memristor demonstrates excellent retention and endurance capabilities. Additionally, by integrating SnS2 transistors, a 1T1R (one transistor and one resistor) structure was constructed, which simplifies circuit design and enables AND gate logic and multivalue logic storage functions. This work establishes a solid foundation for the practical application of 2D high-performance oxide memristors in future high-density-integration and fast in-memory computing systems.

Effect of grain boundaries on metal atom migration and electronic transport in 2D TMD-based resistive switches

Atomic migration from metallic contacts, and subsequent filament formation, is recognised as a prevailing mechanism leading to resistive switching in memristors based on two-dimensional materials (2DMs). This study presents a detailed atomistic examination of the migration of different metal atoms across the grain boundaries (GBs) of 2DMs, employing density functional theory in conjunction with non-equilibrium Green's function transport simulations. Various types of metallic atoms, such as Au, Cu, Al, Ni, and Ag, are examined, focusing on their migration both in the out-of-plane direction through a MoS2 layer and along the surface of the MoS2 layer, pertinent to filament formation in vertical and lateral memristors, respectively. Different types of GBs usually present in MoS2 are considered to assess their influence on the diffusion of metal atoms. The findings are compared with those for structures based on pristine MoS2 and those with mono-sulfur vacancies, aiming to understand the key elements that affect the switching performance of memristors. Furthermore, transport simulations are carried out to evaluate the effects of GBs on both out-of-plane and in-plane electron conductance, providing valuable insights into the resistive switching ratio.

Plasmonic Hot-Electron Effect Enhanced WSe2 Based Transistor Based on Asymmetric Schottky Contacts for Self-Powered Photodetection and Visual Synapse

Schottky interfaces in metal-semiconductor contacts are crucial in optoelectronics, with a focus on enhancing the detection performance. The plasmonic hot-electron effect offers efficient photon-to-electricity conversion, boosting the sensitivity in self-powered photodetectors as well as expanding detection wavelength ranges and improving the functionality of metal-semiconductor-metal (M1-S-M2) structured photodetectors. Utilizing two-dimensional WSe2 nanoflakes, we fabricate a transistor with a M1-S-M2 structure featuring Au and Ag electrodes. Under 405 nm light, we achieve a maximum specific detectivity (D*) of 9.23 × 1011 Jones with a photocurrent density of 4.6 mA cm-2 and a peak on/off ratio of 6.88 × 105. Compared with the Au/WSe2/Au transistor also on the poly(ethylene terephthalate) (PET) substrate, the maximum current density measured for the Au/WSe2/Ag transistor under the light of 405 nm is 1.3 times that of the former, and the D* measured under the light of 1064 nm increases to 20 times the original value; it is now capable of detecting the 1550 nm light, which was undetectable previously. These data clearly demonstrate that the transistor exhibits excellent photodetection performance in the visible and near-infrared spectra. In addition, under biased voltage conditions, the transistor can effectively simulate the visual synaptic behavior under the stimulation of visible light and near-infrared light. Due to its simple structure, wide detection range, excellent light detection performance, and remarkable synaptic plasticity characteristics, this transistor has great potential in various applications of light detection technology and artificial vision systems.

2D MoTe2 memristors for energy-efficient artificial synapses and neuromorphic applications

The potential of two-dimensional (2D) transition metal dichalcogenides (TMDs), especially molybdenum telluride (MoTe2), in sophisticated electrical and low-energy neuromorphic applications, has attracted a lot of interest. The creation, characteristics, and uses of MoTe2-based memristive devices are summarized in this review paper, with an emphasis on their potential as artificial synapses for neuromorphic computing. We thoroughly examine the special properties of MoTe2, such as its remarkable resistance switching response, excellent linearity in synaptic potentiation, and customizable phase states. These characteristics make it possible to implement basic computational functions with minimal energy consumption, including decimal arithmetic operations and the commutative principles of addition and multiplication. In addition to simulating intricate synaptic processes such as long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), the article emphasizes the experimental performances of MoTe2 memristors, which include their capacity to execute exact decimal arithmetic operations. The demonstration of centimeter-scale 2D MoTe2 film-based memristor arrays attaining over 90% recognition accuracy in handwritten digit identification tests further demonstrates the devices' great scalability, stability, and incorporation capabilities. Notwithstanding these developments, issues such as poor environmental robustness, phase transition sensitivity, and low thermal stability still exist. The creation of hybrid or composite materials, doping, and structural alteration are some of the methods to get beyond these obstacles that are covered in the paper. The need for scalable, economical synthesis techniques and a better comprehension of the material's mechanical, optical, and electrical properties through modeling and experiments are emphasized.

Synaptic Plasticity and Memory Retention in ZnO-CNT Nanocomposite Optoelectronic Synaptic Devices

This study presents the fabrication and characterization of ZnO-CNT composite-based optoelectronic synaptic devices via a sol-gel process. By incorporating various concentrations of CNTs (0-2.0 wt%) into ZnO thin films, we investigated their effects on synaptic behaviors under ultraviolet (UV) stimulation. The CNT addition enhanced the electrical and optical performance by forming a p-n heterojunction with ZnO, which promoted charge separation and suppressed recombination. As a result, the 1.5 wt% CNT device exhibited the highest excitatory postsynaptic current (EPSC), improved paired-pulse facilitation, and prolonged memory retention. Learning-forgetting cycles revealed that repeated stimulation reduced the number of pulses required for relearning while extending the forgetting time, mimicking biological memory reinforcement. Energy consumption per pulse was estimated at 16.34 nJ, suggesting potential for low-power neuromorphic applications. A 3 × 3 device array was also employed for visual memory simulation, showing spatially controllable and stable memory states depending on CNT content. To support these findings, structural and optical analyses were conducted using scanning electron microscopy (SEM), UV-visible absorption spectroscopy, photoluminescence (PL) spectroscopy, and Raman spectroscopy. These findings demonstrate that the synaptic characteristics of ZnO-based devices can be finely tuned through CNT incorporation, providing a promising pathway for the development of energy-efficient and adaptive optoelectronic neuromorphic systems.

Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications

In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.

Mixed binary supporting electrolyte approach for enhanced synaptic functionality in one-shot integrable electropolymerized synaptic transistors

The limitations of traditional von Neumann architectures have driven interest in organic mixed ionic-electronic conductors (OMIECs) for integrating memory and computation. Organic electrochemical synaptic transistors (OESTs) are particularly promising for emulating biological synaptic behaviors because they offer low power consumption, flexibility, and scalability. One-shot integrable electropolymerization (OSIEP) has emerged as a promising approach for fabricating OESTs owing to its simplicity and integrative capabilities. However, OSIEP-fabricated devices often exhibit inferior memory characteristics, largely due to suboptimal control of channel crystallinity-a key factor influencing memory retention. In this study, we addressed this challenge by fabricating poly(3,4-ethylenedioxythiphene) (PEDOT)-based OESTs using a mixed binary supporting electrolyte via the OSIEP method. A binary system comprising tetrabutylammonium tetrafluoroborate (BF4-) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (TFSI-) was adopted to balance crystallinity and ionic conductivity. PEDOT:Blend films achieved enhanced synaptic functionality by combining the high de-doping efficiency and charge transport of PEDOT:BF4 with the superior molecular orientation of PEDOT:TFSI. This synergistic approach significantly improved the long-term depression/potentiation characteristics and prolonged memory retention. PEDOT:Blend-based synaptic transistors achieved a recognition accuracy of 95.58% on the MNIST dataset, surpassing devices fabricated with single electrolytes. These findings highlight a scalable strategy for tuning the synaptic properties in OMIEC-based devices, thereby advancing their potential for neuromorphic computing applications.

Neuromorphic Floating-Gate Memory Based on 2D Materials

In recent years, the rapid progression of artificial intelligence and the Internet of Things has led to a significant increase in the demand for advanced computing capabilities and more robust data storage solutions. In light of these challenges, neuromorphic computing, inspired by human brain's architecture and operation principle, has surfaced as a promising answer to the growing technological demands. This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. Two-dimensional (2D) materials, distinguished by their atomic thickness and tunable physical properties, exhibit substantial potential in emulating synaptic plasticity and find broad applications in neuromorphic computing. With respect to device architecture, memory devices based on floating-gate (FG) structures demonstrate robust data retention capabilities and have been widely used in the realm of flash memory. This review begins with a succinct introduction to 2D materials and FG transistors, followed by an in-depth discussion on remarkable research progress in the integration of 2D materials with FG transistors for applications in neuromorphic computing and memory. This paper offers a thorough review of the existing research landscape, encapsulating the notable progress in swiftly expanding field. In conclusion, it addresses the constraints encountered by FG transistors using 2D materials and delineates potential future trajectories for investigation and innovation within this area.

Bipolar Switching Properties and Reaction Decay Effect of BST Ferroelectric Thin Films for Applications in Resistance Random Access Memory Devices

In this manuscript, strontium barium titanate (BST) ferroelectric memory film materials for applications in the feasibility of applying to non-volatile RAM devices were obtained and compared. Solutions were synthesized with a proportional ratio and through the deposition of BST films on titanium nitride/silicon substrates using the sol-gel method, using rapid thermal annealing for defect repair and re-crystallization processing. The crystallization structure and surface morphology of annealed and as-deposited BST films were obtained by XPS, XRD, and SEM measurements. Additionally, the ferroelectric and resistive switching properties for the memory window, the maximum capacitance, and the leakage current were examined for Al/BST/TiN and Cu/BST/TiN structure memory devices. In addition, the first-order reaction equation of the decay reaction behavior for the BST film RRAM devices in the reset state revealed that r=0.19[O2-]1. Finally, the Cu/BST/TiN and Al/BST/TiN structures of the ferroelectric BST films RRAM devices exhibited good memory window properties, bipolar switching properties, and non-volatile properties for applications in non-volatile memory devices.

Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing

Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.

Transformation of 3D Metal-Organic Frameworks into Nanosheets with Enhanced Memristive Behavior for Electronic Data Processing

The transition from three-dimensional (3D) to two-dimensional (2D) semiconducting and insulating materials for micro- and opto-electronics is driven by an energy efficiency and device miniaturization. Herein, the simplicity of growth and stacking of 2D metal-organic framework (MOF) with such planar devices opens up new perspectives in controlling their efficiency and operating parameters. Here, the study reports on 3D to 2D MOF' structural transformation to achieve ultrathin nanosheets with enhanced insulating properties. Based on neutral N-donor ligands, the study designs and solvothermally synthesizes 3D MOFs followed by their thermal and solvent treatment to implement the transformation. A set of single crystal and powder X-ray diffraction, electron microscopy, Raman spectroscopy, numerical modeling, and mechanical exfoliation confirm the nature of the transformation. Compared with initial 3D MOF, its nanosheets demonstrate sufficient changes in electronic properties, expressed as tuning their absorption, photoluminescence, and resistivity. The latter allows to demonstrate the prototype of ultrathin memristive element based on a 4 to 32 nm MOF nanosheet with enhanced functionality (150 to 1400 ON/OFF ratio, retention time exceeding 7300 s, and 100 cycles of switching), thereby, extending the list of scalable and insulating 2D MOFs for micro- and opto-electronics.

Neuromorphic devices for electronic skin applications

Neuromorphic devices represent an important advancement in technology, drawing inspiration from the intricate and efficient mechanisms of the human brain. This review paper elucidates the diverse landscape of neuromorphic electronic skin (e-skin) technologies while highlighting their numerous applications. Here, neuromorphic devices for e-skin are classified as two types of direct neuromorphic e-skins combining both neuromorphic devices and sensors, and indirect e-skins separating neuromorphic devices and sensors. In direct neuromorphic e-skins, there are developing devices like memristor-based neuromorphic devices with sensors and transistor-based neuromorphic devices with sensors. On the other hand, indirect types are demonstrated as separated neuromorphic and sensor parts systems through the various interfacing structures. It also describes recent neuromorphic developments in artificial neural networks (ANNs), deep neural networks (DNNs), and convolutional neural networks (CNNs), for the real-time interpretation of sensory data. Moreover, it introduces multimodal sensory feedback, soft and flexible e-skins, and more intuitive human-machine interfaces. This review examines various applications, including smart textiles for the development of next-generation wearable bioelectronics, brain-sensing interfaces that enhance tactile perception, and the integration of human-machine interfaces aimed at replicating the biological sensorimotor loop, which can improve health monitoring and biomedical applications. Additionally, the review also highlights the potential of neuromorphic e-skin in human-robot interaction, particularly in the context of continuous prosthetic control and robotics. Through this analysis, the paper provides insights into current advancements, identifies key challenges, and suggests future research directions for optimizing neuromorphic e-skin devices and expanding their practical implementation.

Enhanced Resistive Switching and Conduction Mechanisms in Silk Fibroin-Based Memristors with Ag Nanoparticles for Bio-Neuromorphic Applications

This study explores the resistive switching (RS) behavior and conduction mechanisms of Ag/SF-Ag NP/Si memristors with varying Ag NP concentrations. I-V measurements confirm stable RS characteristics across 100 cycles, with consistent set and reset voltages. Increasing Ag NP concentration enhances conductive filament formation, leading to sharper switching transitions and a higher HRS/LRS ratio, w-hich increases from 43 (0 wt% Ag NP) to 4.6 × 104 (10 wt% Ag NP). Log(I)-log(V) analysis reveals a conduction transition from Ohmic to Poole-Frenkel mechanisms, indicating improved charge percolation. Reliability tests show stable LRS values, while HRS exhibits greater variation at higher Ag NP concentrations. These results demonstrate that Ag NPs play a crucial role in optimizing memristor performance, improving switching characteristics, and enhancing reliability. The findings suggest that Ag/SF-Ag NP/Si memristors are promising for high-performance resistive memory and neuromorphic computing applications.

Oxygen Ion Concentration Distribution Effect on Bipolar Switching Properties of Neodymium Oxide Film's Resistance and Random Access Memory Devices

In this study, the bipolar resistance switching behavior and electrical conduction transport properties of a neodymium oxide film's resistive random access memory (RRAM) devices for using different top electrode materials were observed and discussed. Different related electrical properties and transport mechanisms are important factors in applications in a film's RRAM devices. For aluminum top electrode materials, the electrical conduction mechanism of the neodymium oxide film's RRAM devices all exhibited hopping conduction behavior, with 1 mA and 10 mA compliance currents in the set state for low/high voltages applied. For TiN and ITO (Indium tin oxide) top electrode materials, the conduction mechanisms all exhibited ohmic conduction for the low voltage applied, and all exhibited hopping conduction behavior for the high voltage applied. In addition, the electrical field strength simulation resulted in an increase in the reset voltage, indicating that oxygen ions have diffused into the vicinity of the ITO electrode during the set operation. This was particularly the case in the three physical models proposed, and based on the relationship between different ITO electrode thicknesses and the oxygen ion concentration distribution effect of the neodymium oxide film's RRAM devices, they were investigated and discussed. To prove the oxygen concentration distribution expands over the area of the ITO electrode, the simulation software was used to analyze and simulate the distribution of the electric field for the Poisson equation. Finally, the neodymium oxide film's RRAM devices for using different top electrode materials all exhibited high memory window properties, bipolar resistance switching characteristics, and non-volatile properties for incorporation into next-generation non-volatile memory device applications in this study.

Multifunctional Artificial Electric Synapse of MoSe2-Based Memristor toward Neuromorphic Application

Research on memristive devices to seamlessly integrate and replicate the dynamic behaviors of biological synapses will illuminate the mechanisms underlying parallel processing and information storage in the human brain, thereby affording novel insights for the advancement of artificial intelligence. Here, an artificial electric synapse is demonstrated on a one-step Mo-selenized MoSe2 memristor, having not only long-term stable resistive switching characteristics (reset 0.51 ± 0.01 V, on/off ratio > 30, retention > 103 s) but also diverse electrically adjustable synaptic behaviors, including multilevel conductance (synaptic weight), excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/D), spike-timing-dependent plasticity (STDP), and especially activity-dependent synaptic plasticity (ADSP). More significantly, neuromorphic functions of both image edge extraction and biological perception imitation have been successfully achieved. These results present a promising design toward synaptic devices for advancing neuromorphic systems with integrated brain-like neural sensing, memory, and recognition.

Exploring Moiré Superlattices and Memristive Switching in Non-van der Waals Twisted Bilayer Bi2O2Se

The discovery of moiré physics in two-dimensional (2D) materials has opened new avenues for exploring unique physical and chemical properties induced by intralayer/interlayer interactions. This study reports the experimental observation of moiré patterns in 2D bismuth oxyselenide (Bi2O2Se) nanosheets grown through one-pot chemical reaction methods and a sonication-assisted layer separations technique. Our findings demonstrate that these moiré patterns result from the angular stacking of the nanosheets at various twist angles, leading to the formation of moiré superlattices (MSLs) with distinct periodicities. The presence of these superlattices was confirmed using transmission electron microscopy (TEM) images. The observation of moiré patterns in 2D Bi2O2Se nanosheets highlights the potential of tuning the band structures of the non-van der Waals material and thus unlocking new material properties through precise control of intralayer/interlayer interactions. Furthermore, the stacked 2D Bi2O2Se nanosheets show interesting memristive switching characteristics, presenting a promising candidate for artificial synapses and neuromorphic computing. Traditional memristors typically utilize a vertical metal-insulator-metal (MIM) structure, which relies on the formation of conductive filaments for resistive switching (RS). This configuration, however, often results in abrupt switching during various cycles and significant variation from device to device. Herein, defective BOSe moiré material exhibits a nonfilamentary RS switching characteristic in a two-terminal lateral device configuration. This design reveals an RS mechanism driven by the modulation of the Schottky barrier height (SBH) due to the movement of Se vacancies (VSe) under an external electric field. The fabricated device exhibits excellent RS behavior, achieving an RS ratio of ∼20 with a high degree of control and consistency across multiple cycles and from device to device. Interestingly, the device shows a stable negative differential resistance effect in the high-voltage region due to the carrier trapping process. Finally, we studied the stability of the MSL in BOSe through TEM imaging and electrical characterization on different device configurations to evaluate the repeatability of the switching characteristics.

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks

The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial synaptic devices enhanced with graphene quantum dots (GQDs) that exhibit metaplasticity is introduced, a higher-order form of synaptic plasticity that facilitates the dynamic regulation of memory and learning processes similar to those observed in biological systems. The GQDs-assisted devices utilize interface-mediated modifications in asymmetric conductive pathways, replicating classical synaptic plasticity mechanisms. This allows for repeatable and linearly programmable adjustments to future weight changes linked to historical weights. Incorporating metaplasticity is essential for achieving generalization within deep neural networks, which enables them to adapt more fluidly to new information while retaining previously acquired knowledge. The GQDs-device-based system achieved a 97% accuracy on the fourth MNIST dataset task, while consistently achieving performance levels above 94% on prior tasks. This performance substantiates the feasibility of directly transferring metaplasticity principles to deep neural networks, thereby addressing the challenges associated with catastrophic forgetting. These findings present a promising hardware solution for developing neuromorphic systems with robust and sustained learning capabilities that can effectively bridge the gap between artificial and biological neural networks.

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.

Chiroptical Synaptic Perovskite Memristor as Reconfigurable Physical Unclonable Functions

Physical unclonable functions (PUFs), often referred to as digital fingerprints, are emerging as critical elements in enhancing hardware security and encryption. While significant progress has been made in developing optical and memory-based PUFs, integrating reconfigurability with sensitivity to circularly polarized light (CPL) remains largely unexplored. Here, we present a chiroptical synaptic memristor (CSM) as a reconfigurable PUF, leveraging a two-dimensional organic-inorganic halide chiral perovskite. The device combines CPL sensitivity with photoresponsive electrical behavior, enabling its application in optoneuromorphic systems, as demonstrated by its ability to perform image categorization tasks within neuromorphic computing. Furthermore, by leveraging a 10 × 10 crossbar array of the CSMs, we develop a PUF capable of generating reconfigurable cryptographic keys based on the combination of neuromorphic potentiation and polarized light conditions. This work demonstrates an integrated approach to optoneuromorphic functionality, data storage, and encryption, providing an alternative approach for reconfigurable memristor-based PUFs.

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.

Recent progress in two-dimensional Bi2O2Se and its heterostructures

Ever since the identification of graphene, research on two-dimensional (2D) materials has garnered significant attention. As a typical layered bismuth oxyselenide, Bi2O2Se has attracted growing interest not only due to its conventional thermoelectricity but also because of the excellent optoelectronic properties found in the 2D limit. Moreover, 2D Bi2O2Se exhibits remarkable properties, including high carrier mobility, air stability, tunable band gap, unique defect characteristics, and favorable mechanical properties. These properties make it a promising candidate for next-generation electronic and optoelectronic devices, such as logic devices, photodetectors, sensors, energy technologies, and memory devices. However, despite significant progress, there are still challenges that must be addressed for widespread commercial use. This review provides an overview of progress in Bi2O2Se research. We start by introducing the crystal structure and physical properties of Bi2O2Se and a compilation of methods for modulating its physical properties is further outlined. Then, a series of methods for synthesizing high-quality 2D Bi2O2Se are summarized and compared. We next focus on the advancements made in the practical applications of Bi2O2Se in the fields of field-effect transistors (FETs), photodetectors, neuromorphic computing and optoelectronic synapses. As heterostructures induce a new degree of freedom to modulate the properties and broaden applications, we especially discuss the heterostructures and corresponding applications of Bi2O2Se integrated with 0D, 1D and 2D materials, providing insights into constructing heterojunctions and enhancing device performance. Finally, the development prospects for Bi2O2Se and future challenges are discussed.

Non-Volatile Resistive Switching in Nanoscaled Elemental Tellurium by Vapor Transport Deposition on Gold

Two-dimensional (2D) materials are promising for resistive switching in neuromorphic and in-memory computing, as their atomic thickness substantially improve the energetic budget of the device and circuits. However, many 2D resistive switching materials struggle with complex growth methods or limited scalability. 2D tellurium exhibits striking characteristics such as simplicity in chemistry, structure, and synthesis making it suitable for various applications. This study reports the first memristor design based on nanoscaled tellurium synthesized by vapor transport deposition (VTD) at a temperature as low as 100 °C fully compatible with back-end-of-line processing. The resistive switching behavior of tellurium nanosheets is studied by conductive atomic force microscopy, providing valuable insights into its memristive functionality, supported by microscale device measurements. Selecting gold as the substrate material enhances the memristive behavior of nanoscaled tellurium in terms of reduced values of set voltage and energy consumption. In addition, formation of conductive paths leading to resistive switching behavior on the gold substrate is driven by gold-tellurium interface reconfiguration during the VTD process as revealed by energy electron loss spectroscopy analysis. These findings reveal the potential of nanoscaled tellurium as a versatile and scalable material for neuromorphic computing and underscore the influential role of gold electrodes in enhancing its memristive performance.

Orientation-Selective Memory Switching in Quasi-1D NbSe3 Neuromorphic Device for Omnibearing Motion Detection

Intelligent neuromorphic hardware holds considerable promise in addressing the growing demand for massive real-time data processing in edge computing. Resistive switching materials with intrinsic anisotropy and a compact design of non-volatile memory devices with the capability of handling spatiotemporally reconstructed data is crucial to perform sophisticated tasks in complex application scenarios. In this study, an anisotropic resistive switching cell with a planar configuration based on lithiated NbSe3 nanosheets is demonstrated. Benefitting from the highly aligned diffusive channel associated with a quasi-1D van der Waals structure, the memristor patterned along NbSe3 atomic chains presents robust memory switching behavior with superior stability, particularly the low set/reset voltages (0.4 V/-0.36 V) and extremely small standard deviation (0.041 V/0.051 V), among the best compared to state-of-the-art devices. More importantly, unlike traditional resistive switching materials, anisotropic ion migration in NbSe3 crystals leads to a high orientation selectivity in the conductance update. Custom-designed neuromorphic hardware contributes to the implementation of omnibearing motion recognition for automatic pilot applications, yielding a high accuracy of 95.9% considering variations. This article presents a new strategy based on NbSe3 crystals to develop a neuromorphic computing system with intelligent application scenarios.

Dynamic FeOx/FeWOx nanocomposite memristor for neuromorphic and reservoir computing

Memristors are crucial in computing due to their potential for miniaturization, energy efficiency, and rapid switching, making them particularly suited for advanced applications such as neuromorphic computing and in-memory operations. However, these tasks often require different operational modes-volatile or nonvolatile. This study introduces a forming-free Ag/FeOx/FeWOx/Pt nanocomposite memristor capable of both operational modes, achieved through compliance current (CC) adjustment and structural engineering. Volatile switching occurs at low CC levels (<500 μA), transitioning to nonvolatile at higher levels (mA). Operating at extremely low voltages (<0.2 V), this memristor exhibits excellent uniformity, data retention, and multilevel switching, making it highly suitable for high-density data storage. The memristor successfully mimics fundamental biological synapse functions, exhibiting potentiation, depression, and spike-rate dependent plasticity (SRDP). It effectively emulates transitions from short-term memory (STM) to long-term memory (LTM) by varying pulse characteristics. Leveraging its volatile switching and STM features, the memristor proves ideal for reservoir computing (RC), where it can emulate dynamic reservoirs for sequence data classification. A physical RC system, implemented using digits 0 to 9, achieved a recognition rate of 93.4% in off-chip training with a deep neural network (DNN), confirming the memristor's effectiveness. Overall, the dual-mode switching capability of the Ag/FeOx/FeWOx/Pt memristor enhances its potential for AI applications, particularly in temporal and sequential data processing.

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.

Self-Rectifying Memristor-Based Reservoir Computing for Real-Time Intrusion Detection in Cybersecurity

The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO3-x/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.

Enhanced Electrical Polarization in van der Waals α-In2Se3 Ferroelectric Semiconductor Field-Effect Transistors by Eliminating Surface Screening Charge

A van der Waals (vdW) α-In2Se3 ferroelectric semiconductor channel-based field-effect transistor (FeS-FET) has emerged as a next-generation electronic device owing to its versatility in various fields, including neuromorphic computing, nonvolatile memory, and optoelectronics. However, screening charges cause by the imperfect surface morphology of vdW α-In2Se3 inhibiting electrical polarization remain an unresolved issue. In this study, for the first time, a method is elucidated to recover the inherent electric polarization in both in- and out-of-plane directions of the α-In2Se3 channel based on post-exfoliation annealing (PEA) and improve the electrical performance of vdW FeS-FETs. Following PEA, an ultra-thin In2Se3-3xO3x layer formed on the top surface of the α-In2Se3 channel is demonstrated to passivate surface defects and enhance the electrical performance of FeS-FETs. The on/off current ratio of the α-In2Se3 FeS-FET has increased from 5.99 to 1.84 × 106, and the magnitude of ferroelectric resistance switching has increased from 1.20 to 26.01. Moreover, the gate-modulated artificial synaptic operation of the α-In2Se3 FeS-FET is demonstrated and illustrate the significance of the engineered interface in the vdW FeS-FET for its application to multifunctional devices. The proposed α-In2Se3 FeS-FET is expected to provide a significant breakthrough for advanced memory devices and neuromorphic computing.

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.

High-Performance Memristive Synapse Based on Space-Charge-Limited Conduction in LiNbO3

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO3/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (VO-VCM). The voltage-controlled VO-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of VO-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the VO-VCM-based Al/LiNbO3/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device.

Wafer-Scale Ag2S-Based Memristive Crossbar Arrays with Ultra-Low Switching-Energies Reaching Biological Synapses

Memristive crossbar arrays (MCAs) offer parallel data storage and processing for energy-efficient neuromorphic computing. However, most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor (CMOS) technology still suffer from substantially larger energy consumption than biological synapses, due to the slow kinetics of forming conductive paths inside the memristive units. Here we report wafer-scale Ag2S-based MCAs realized using CMOS-compatible processes at temperatures below 160 °C. Ag2S electrolytes supply highly mobile Ag+ ions, and provide the Ag/Ag2S interface with low silver nucleation barrier to form silver filaments at low energy costs. By further enhancing Ag+ migration in Ag2S electrolytes via microstructure modulation, the integrated memristors exhibit a record low threshold of approximately - 0.1 V, and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses. The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics. Moreover, the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm. An impressive accuracy of 92.6% in image recognition simulations is demonstrated with the MCAs after the compensation. The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.

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

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

Synaptic-like plasticity in 2D nanofluidic memristor from competitive bicationic transport

Synaptic plasticity, the dynamic tuning of signal transmission strength between neurons, serves as a fundamental basis for memory and learning in biological organisms. This adaptive nature of synapses is considered one of the key features contributing to the superior energy efficiency of the brain. Here, we use molecular dynamics simulations to demonstrate synaptic-like plasticity in a subnanoporous two-dimensional membrane. We show that a train of voltage spikes dynamically modifies the membrane's ionic permeability in a process involving competitive bicationic transport. This process is shown to be repeatable after a given resting period. Because of a combination of subnanometer pore size and the atomic thinness of the membrane, this system exhibits energy dissipation of 0.1 to 100 aJ per voltage spike, which is several orders of magnitude lower than 0.1 to 10 fJ per spike in the human synapse. We reveal the underlying physical mechanisms at molecular detail and investigate the local energetics underlying this apparent synaptic-like behavior.

Advances and Applications of Oxidized van der Waals Transition Metal Dichalcogenides

The surface oxidation of 2D transition metal dichalcogenides (TMDs) has recently gained tremendous technological and fundamental interest owing to the multi-functional properties that the surface oxidized layer opens up. In particular, when integrated into other 2D materials in the form of van der Waals heterostructures, oxidized TMDs enable designer properties, including novel electronic states, engineered light-matter interactions, and exceptional-point singularities, among many others. Here, the evolving landscapes of the state-of-the-art surface engineering technologies that enable controlled oxidation of TMDs down to the monolayer-by-monolayer limit are reviewed. Next, the use of oxidized TMDs in van der Waals heterostructures for different electronic and photonic device platforms, materials growth processes, engineering concepts, and synthesizing new condensed matter phenomena is discussed. Finally, challenges and outlook for future impact of oxidized TMDs in driving rapid advancements across various application fronts is discussed.

Configurable Synaptic and Stochastic Neuronal Functions in ZnTe-Based Memristor for an RBM Neural Network

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (µA) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.

Reconfigurable Homojunction Phototransistor for Near-Zero Power Consumption Artificial Biomimetic Retina Function

Semiconductor photodetectors integrating preliminary signal-processing functions play a vital role in artificial biomimetic retina systems. Herein, we propose a tungsten diselenide (WSe2) phototransistor with a dual-layer gate dielectric and an asymmetric graphene insert structure. This phototransistor exhibits a bidirectional self-powered photocurrent by controlling the gate voltage via the formation of reconfigurable p+-p and n-p homojunctions in the channel from the asymmetric graphene insert. At the same time, the nonvolatile electron and hole stored in the dual-layer gate dielectric are generated using a temporary gate voltage, which can replace the gate voltage to regulate the channel charge. Moreover, the photocurrent shows a linear relation with the temporary programming gate voltage. The phototransistor exhibits a rectification ratio of >4 orders of magnitude without the gate voltage, indicating its significant capability to operate in a fully self-powered mode with near-zero power consumption. Based on the device characteristics, we successfully simulate the biological functions of the photoreceptor layer and bipolar cell layer in the retinal receptive field. The identification of the object motion direction in the receptive field can be realized by integrating three programmable devices on the chip. Furthermore, edge enhancement of the image is achieved by independently modulating the light response of each pixel in the sensor by varying the programming gate voltage. This study will promote the developing progress of future artificial biomimetic retina systems.

Memristors with Monolayer Graphene Electrodes Grown Directly on Sapphire Wafers

The development of the memristor has generated significant interest due to its non-volatility, simple structure, and low power consumption. Memristors based on graphene offer atomic monolayer thickness, flexibility, and uniformity and have attracted attention as a promising alternative to contemporary field-effect transistor (FET) technology in applications such as logic and memory devices, achieving higher integration density and lower power consumption. The use of graphene as electrodes in memristors could also increase robustness against degradation mechanisms, including oxygen vacancy diffusion to the electrode and unwanted metal ion diffusion. However, to realize this technological transformation, it is necessary to establish a scalable, robust, and cost-effective device fabrication process. Here we report the direct growth of high-quality monolayer graphene on sapphire wafers in a mass-producible, contamination-free, and transfer-free manner, using a commercially available metal-organic chemical vapor deposition (MOCVD) system. By taking advantage of this approach, graphene-electrode based memristors are developed, and all the processes used in the device fabrication incorporating graphene electrodes can be performed at wafer scale. The graphene electrode-based memristor demonstrates promising characteristics in terms of endurance, retention, and ON/OFF ratio. This work presents a possible and viable route to achieving robust graphene-based memristors in a commercially and technologically sustainable manner, paving the way for the realization of more powerful and compact integrated graphene electronics in the future.

Leveraging volatile memristors in neuromorphic computing: from materials to system implementation

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

Mechanisms of Defect-Mediated Memristive Behavior in MoS2 Monolayer

The switching dynamics of a Au∥VS2@MoS2 atomristor is explored by first-principles computations of the atomic-configuration energy and electron transport. It is found that external bias can reduce the energy barrier between the two (high- and low-) conduction states, to achieve nonvolatile resistive switching. We find that the force acting on the switching atom is a combination of electrostatic force (while its charge is induced both electrostatically and chemically) and also by electron-wind, whose effect may hinder the writing process at larger bias. The analysis uncovers how the writing and reading processes of the atomristor depend on several factors: (i) atomic structure details of the Au tip; (ii) the space-gap distance between the tip and MoS2 layer; and (iii) tip metal choice. The fundamental understanding of switching events provides useful guidance for memristor design and possible limitations.

Simulating and Implementing Broadband van der Waals Artificial Visual Synapses Based on Photoconductivity and Pyroconductivity Mechanisms

With advancements in artificial neural networks and information processing technology, a variety of neuromorphic synaptic devices have been proposed to emulate human sensory systems, with vision being a crucial information source. Moreover, as practical applications become increasingly complex, the need for multifunctional visual synapses to expand the application range becomes urgent. This study introduces a MoS2/WSe2 van der Waals (vdW) heterojunction and utilizes it to replicate artificial visual synapses by harnessing the cooperative effect of photoconductivity and pyroconductivity mechanisms. By adjusting the optical power, pulse width, and pulse number of the optical stimulus, the heterojunction effectively simulates synaptic properties. Under the combined action of an external electric field and the built-in electric field (Ebi), the heterojunction exhibits broadband synaptic properties in the visible to near-infrared spectrum (405-1550 nm) while consuming low power of 0.3-1.1 pJ per spike. The heterojunction can detect ultraweak optical signals at 660 nm with an optical power intensity of 14 μW/cm2, displaying a high specific detectivity (D*) of 3.98 × 1011 Jones. Furthermore, at 405, 808, 1064, and 1550 nm, the D* of the heterojunction is 4.16 × 1011, 3.61 × 109, 4.96 × 107, and 1.64 × 107 Jones, respectively. Visual synaptic devices based on the MoS2/WSe2 vdW heterojunction hold significant promise for the future development of integrated sensing and memory processing devices.

Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS2 and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 103, 100 times larger than that of devices prepared with undoped WS2. The Nb atoms in WS2 play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS2 synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study's contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

A Flexible Laser-Induced Graphene Memristor with Volatile Switching for Neuromorphic Applications

Two-dimensional graphene and graphene-based materials are attracting increasing interest in neuromorphic computing applications by the implementation of memristive architectures that enable the closest solid-state equivalent to biological synapses and neurons. However, the state-of-the-art fabrication methodology involves routine use of high-temperature processes and multistepped chemical synthesis, often on a rigid substrate constraining the experimental exploration in the field to high-tech facilities. Here, we demonstrate the use of a one-step process using a commercial laser to fabricate laser-induced graphene (LIG) memristors directly on a flexible polyimide substrate. For the first time, a volatile resistive switching phenomenon is reported in the LIG without using any additional materials. The absence of any precursor or patterning mask greatly simplifies the process while reducing the cost and providing greater controllability. The fabricated memristors show multilevel resistance-switching characteristics with high endurance and tunable timing characteristics. The recovery time and the trigger pulse-dependent state change are shown to be highly suitable for its use as a synaptic element and in the realization of leaky-integrate and fire neuron in neuromorphic circuits.

Memristive properties and synaptic plasticity in substituted pyridinium iodobismuthates

This study explores the impact of organic cations in bismuth iodide complexes on their memristive behavior in metal-insulator-metal (MIM) type thin-layer devices. The presence of electron-donating and electron withdrawing functional groups (-CN, -CH3, -NH2, and -N(CH3)2) on pyridinium cations induces morphological alterations in crystals, thus influencing the electronic or ionic conductivity of devices comprising sandwiched thin layers (thickness = 200 nm ± 50) between glass/ITO as bottom electrode (∼110 nm) and copper (∼80 nm) as the top electrode. It was found that the current-voltage (I-V) scans of the devices reveal characteristic pinched hysteresis loops, a distinct signature of memristors. The working voltage windows are significantly influenced by both the types of cation and the dimensionality of ionic fragments (0D or 1D) in the solid-state form. Additionally, the temperature alters the surface area of the I-V loops by affecting resistive switching mechanisms, corresponding log-log plots at three temperatures (-30 °C, room temperature and 150 °C) are fully studied. Given that a memristor can operate as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigates long-term depression, potentiation, and spike-time-dependent plasticity-a specific form of the Hebbian learning rule-to mimic biologically synaptic plasticity. Different polar pulses, such as triangle, sawtooth, and square waveforms were employed to generate Hebbian learning rules. The research demonstrates how the shape of the applied pulse series, achieved by overlapping pre- and post-pulses at different time scales, in association with the composition and dimensionality of ionic fragments, lead to changes in the synaptic weight percentages of the devices.

Spiking Neural Network Integrated with Impact Ionization Field-Effect Transistor Neuron and a Ferroelectric Field-Effect Transistor Synapse

The integration of artificial spiking neurons based on steep-switching logic devices and artificial synapses with neuromorphic functions enables an energy-efficient computer architecture that mimics the human brain well, known as a spiking neural network (SNN). 2D materials with impact ionization or ferroelectric characteristics have the potential for use in such devices. However, research on 2D spiking neurons remains limited and investigations of 2D artificial synapses far more common. An innovative 2D spiking neuron is implemented using a WSe2 impact ionization transistor (I2FET), while a spiking neural network is formed by combining it with a 2D ferroelectric synaptic device (FeFET). The suggested 2D spiking neuron demonstrates precise spiking behavior that closely resembles that of actual neurons. In addition, it achieves a low energy consumption of 2 pJ/spike. The better impact ionization properties of WSe2 are responsible for this efficiency. Furthermore, an all-2D SNN consisting of 2D I2FET neurons and 2D FeFET synapses is constructed, which achieves high accuracy of 87.5% in a face classification task by unsupervised learning. The integration of a 2D SNN with 2D steep-switching spiking neuronal devices and 2D synaptic devices shows great potential for the development of neuromorphic systems with improved energy efficiency and computational capabilities.

Programmable Optoelectronic Synaptic Transistors Based on MoS2/Ta2NiS5 Heterojunction for Energy-Efficient Neuromorphic Optical Operation

The optoelectronic synaptic transistors with various functions, broad spectral perception, and low power consumption are an urgent need for the development of advanced optical neural network systems. However, it remains a great challenge to realize the functional diversification of the systems on a single device. 2D van der Waals (vdW) materials can combine unique properties by stacking with each other to form heterojunctions, which may provide a strategy for solving this problem. Herein, an all-2D vdW heterojunction-based programmable optoelectronic synaptic transistor based on MoS2/Ta2NiS5 heterojunctions is demonstrated. The device implements reconfigurable, multilevel non-volatile memory (NVM) states through sequential modulation of multiple optical and electrical stimuli to achieve broadband (532-808 nm), energy-efficient (17.2 fJ), hetero-synaptic functionality in a bionic manner. The intrinsic working mechanisms of the photogating effect caused by band alignment and the interfacial trapping defect modulation induced by gate voltage are revealed by Kelvin-probe force microscopy (KPFM) measurements and carrier transport analysis. Overall, the (opto)electronic synaptic weight controllability for combined in-sensor and in-memory logic processors is realized by the heterojunction properties. The proposed findings facilitate the technical realization of generic all 2D hetero-synapses for future artificial vision systems, opto-logical systems, and Internet of Things (IoT) entities.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.