Memristor Based on Inorganic and Organic Two-Dimensional Materials: Mechanisms, Performance, and Synaptic Applications

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.

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.

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.

Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

Doping Detection Based on the Nanoscale: Biosensing Mechanisms and Applications of Two-Dimensional Materials

Doping undermines fairness in sports and threatens athlete health, while conventional detection methods like LC-MS and GC-MS face challenges such as complex procedures, matrix interferences, and lengthy processing times, limiting on-site applications. Two-dimensional (2D) materials, including graphene, MoS2, and metal-organic frameworks (MOFs), offer promising solutions due to their large surface areas, tunable electronic structures, and special interactions with doping agents, such as hydrogen bonding, π-π stacking, and electrostatic forces. These materials enable signal transduction through changes in conductivity or fluorescence quenching. This review highlights the use of 2D materials in doping detection. For example, reduced graphene oxide-MOF composites show high sensitivity for detecting anabolic steroids like testosterone, while NiO/NGO nanocomposites exhibit strong selectivity for stimulants like ephedrine. However, challenges such as environmental instability and high production costs hinder their widespread application. Future efforts should focus on improving material stability through chemical modifications, reducing production costs, and integrating these materials into advanced systems like machine learning. Such advancements could revolutionize doping detection, ensuring fairness in sports and protecting athlete health.

Recent Progress on Heterojunction-Based Memristors and Artificial Synapses for Low-Power Neural Morphological Computing

Memristors and artificial synapses have attracted tremendous attention due to their promising potential for application in the field of neural morphological computing, but at the same time, continuous optimization and improvement in energy consumption are also highly desirable. In recent years, it has been demonstrated that heterojunction is of great significance in improving the energy consumption of memristors and artificial synapses. By optimizing the material composition, interface characteristics, and device structure of heterojunctions, energy consumption can be reduced, and performance stability and durability can be improved, providing strong support for achieving low-power neural morphological computing systems. Herein, we review the recent progress on heterojunction-based memristors and artificial synapses by summarizing the working mechanisms and recent advances in heterojunction memristors, in terms of material selection, structure design, fabrication techniques, performance optimization strategies, etc. Then, the applications of heterojunction-based artificial synapses in neuromorphological computing and deep learning are introduced and discussed. After that, the remaining bottlenecks restricting the development of heterojunction-based memristors and artificial synapses are introduced and discussed in detail. Finally, corresponding strategies to overcome the remaining challenges are proposed. We believe this review may shed light on the development of high-performance memristors and artificial synapse devices.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

Combined optical and electrical control of a low-power consuming (∼fJ) two-terminal organic artificial synapse for associative learning and neuromorphic applications

Optoelectronic synaptic devices outperform electrical synapses in speed, energy efficiency, and integration density. Recent progress in visual sensing and optogenetics has led to the integration of light-sensitive materials in these devices, promising unmatched speed, connectivity, and bandwidth. Here, we present a copper phthalocyanine (CuPc) based optoelectronic synaptic device boasting femto Joule power consumption stable at room temperature. The optoelectronic synapse can be operated with energy consumption as low as 430.4 fJ which is very attractive from the point of view of low-power neuromorphic devices. By modulating light pulses, the neuromorphic behavior can be emulated including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), transitioning from short-term plasticity (STP) to long-term plasticity (LTP), spike-rate dependent plasticity (SRDP) and spike-number dependent plasticity (SNDP), etc. Optical potentiation and electrical depression are observed with combined optical and electrical stimulation, proving the multi-functionality of the synapse. Furthermore, the device demonstrates classical associative learning behaviors like Pavlovian conditioning using optical and electrical stimuli. We have established the pain conditioning processes such as hyperalgesic response and pain extinction effects with varying optical pulse amplitudes. These results render the CuPc-based devices as multifunctional and highly versatile artificial synaptic devices for future computing applications, offering unprecedented efficiency and functionality in neuromorphic systems.

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.

Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability

During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).

2D Halide Perovskites for High-Performance Resistive Switching Memory and Artificial Synapse Applications

Metal halide perovskites (MHPs) are considered as promising candidates in the application of nonvolatile high-density, low-cost resistive switching (RS) memories and artificial synapses, resulting from their excellent electronic and optoelectronic properties including large light absorption coefficient, fast ion migration, long carrier diffusion length, low trap density, high defect tolerance. Among MHPs, 2D halide perovskites have exotic layered structure and great environment stability as compared with 3D counterparts. Herein, recent advances of 2D MHPs for the RS memories and artificial synapses realms are comprehensively summarized and discussed, as well as the layered structure properties and the related physical mechanisms are presented. Furthermore, the current issues and developing roadmap for the next-generation 2D MHPs RS memories and artificial synapse are elucidated.

High-Density, Nonvolatile, Flexible Multilevel Organic Memristor Using Multilayered Polymer Semiconductors

Nonvolatile organic memristors have emerged as promising candidates for next-generation electronics, emphasizing the need for vertical device fabrication to attain a high density. Herein, we present a comprehensive investigation of high-performance organic memristors, fabricated in crossbar architecture with PTB7/Al-AlOx-nanocluster/PTB7 embedded between Al electrodes. PTB7 films were fabricated using the Unidirectional Floating Film Transfer Method, enabling independent uniform film fabrication in the Layer-by-Layer (LbL) configuration without disturbing underlying films. We examined the charge transport mechanism of our memristors using the Hubbard model highlighting the role of Al-AlOx-nanoclusters in switching-on the devices, due to the accumulation of bipolarons in the semiconducting layer. By varying the number of LbL films in the device architecture, the resistance of resistive states was systematically altered, enabling the fabrication of novel multilevel memristors. These multilevel devices exhibited excellent performance metrics, including enhanced memory density, high on-off ratio (>108), remarkable memory retention (>105 s), high endurance (87 on-off cycles), and rapid switching (∼100 ns). Furthermore, flexible memristors were fabricated, demonstrating consistent performance even under bending conditions, with a radius of 2.78 mm for >104 bending cycles. This study not only demonstrates the fundamental understanding of charge transport in organic memristors but also introduces novel device architectures with significant implications for high-density flexible applications.

1-Selector 1-Memristor Configuration with Multifunctional a-IGZO Memristive Devices Fabricated at Room Temperature

Serving as neuromorphic hardware accelerators, memristors play a crucial role in large-scale neuromorphic computing. Herein, two-terminal memristors utilizing amorphous indium-gallium-zinc oxide (a-IGZO) are fabricated through room-temperature sputtering. The electrical characteristics of these memristors are effectively modulated by varying the oxygen flow during the deposition process. The optimized a-IGZO memristor, fabricated under 3 sccm oxygen flow, presents a 5 × 103 ratio between its high- and low-resistance states, which can be maintained over 1 × 104 s with minimal degradation. Meanwhile, desirable properties such as electroforming-free and self-compliance, crucial for low-energy consumption, are also obtained in the a-IGZO memristor. Moreover, analog conductance switching is observed, demonstrating an interface-type behavior, as evidenced by its device-size-dependent performance. The coexistence of negative differential resistance with analog switching is attributed to the migration of oxygen vacancies and the trapping/detrapping of charges. Furthermore, the device demonstrates optical storage capabilities by exploiting the optical properties of a-IGZO, which can stably operate for up to 50 sweep cycles. Various synaptic functions have been demonstrated, including paired-pulse facilitation and spike-timing-dependent plasticity. These functionalities contribute to a simulated recognition accuracy of 90% for handwritten digits. Importantly, a one-selector one-memristor (1S1M) architecture is successfully constructed at room temperature by integrating a-IGZO memristor on a TaOx-based selector. This architecture exhibits a 107 on/off ratio, demonstrating its potential to suppress sneak currents among adjacent units in a memristor crossbar.

Exciton Dynamics of TiOPc/WSe2 Heterostructure

The van der Waals (vdW) heterostructures composed of two-dimensional (2D) transition metal dichalcogenides (TMDs) and organic semiconductors demonstrate numerous compelling optoelectronic properties. However, the influence of the vdW epitaxial effect and temperature on the optoelectronic properties and interface exciton dynamics of heterostructures remains unclear. This study systematically investigates the fluorescence properties of TiOPc/WSe2 heterostructure. Comprehensive spectral characterization elucidates that the emission behavior of the TiOPc/WSe2 heterostructure arises from charge/energy transfer at the heterostructure interfaces and the structural ordering of the organic layer on the 2D monolayer WSe2 induced by vdW epitaxy. The interface exciton dynamic features probed by ultrafast transient spectroscopy reveal that the face-to-face molecular stacking configuration of TiOPc exhibits ultrafast exciton dynamics. In particular, we observe picosecond-scale absorption of organic molecular dimer cations, providing direct evidence of interface charge transfer at room temperature. Moreover, energy transfer from the TiOPc to WSe2 may exist based on the tunability in the fluorescence emission of the TiOPc/WSe2 heterostructure as the temperature changes. This study unveils the critical role of vdW epitaxy and temperature in the exciton dynamics of organic/2D TMDs hybrid systems and provides guidance for studying interlayer charge and energy transfer in organic/inorganic heterostructures.

Group III (In/Ga)-V (P/As)-VI (S/Se) Monolayers: A New Class of Auxetic Semiconductors with Highly Anisotropic Electronic/Optical/Mechanical/Thermal Properties

We present a theoretical design of a class of 2D semiconducting materials, namely, group III (In/Ga)-V (P/As)-VI (S/Se) monolayers, whose global-minimum structures are predicted based on the particle swarm optimization method. Electronic structure calculations suggest that all group III-V-VI monolayers exhibit quasi-direct semiconducting characteristics with desirable band gaps ranging from 1.76 to 2.86 eV (HSE06 functional). Moreover, most group III-V-VI monolayers possess highly anisotropic carrier mobilities with large anisotropic ratios (3.4-6 for electrons, 2.2-25 for holes). G0W0+BSE calculations suggest that these monolayers show high optical anisotropy and relatively large exciton binding energies (0.33-0.75 eV), comparable to that (0.5 eV) of MoS2 monolayer. In particular, the GaPS monolayer manifests strikingly anisotropic I-V curves with a large ON/OFF ratio of ∼105 (106 for the GaPS bilayer) and anisotropic lattice thermal conductivity. Furthermore, the GaPS monolayer is predicted to exhibit both in-plane and out-of-plane negative Poisson ratios (NPRs) and prominent anisotropic Young moduli.

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

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.

MoS2-Based Memristor: Robust Resistive Switching Behavior and Reliable Biological Synapse Emulation

Memristors are recognized as crucial devices for future nonvolatile memory and artificial intelligence. Due to their typical neuron-synapse-like metal-insulator-metal(MIM) sandwich structure, they are widely used to simulate biological synapses and have great potential in advancing biological synapse simulation. However, the high switch voltage and inferior stability of the memristor restrict the broader application to the emulation of the biological synapse. In this study, we report a vertically structured memristor based on few-layer MoS2. The device shows a lower switching voltage below 0.6 V, with a high ON/OFF current ratio of 104, good stability of more than 180 cycles, and a long retention time exceeding 3 × 103 s. In addition, the device has successfully simulated various biological synaptic functions, including potential/depression propagation, paired-pulse facilitation (PPF), and long-term potentiation/long-term depression (LTP/LTD) modulation. These results have significant implications for the design of a two-dimensional transition-metal dichalcogenides composite material memristor that aim to mimic biological synapses, representing promising avenues for the development of advanced neuromorphic computing systems.

Reliable Memristive Synapses Based on Parylene-MoOx Nanocomposites for Neuromorphic Applications

Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.

The Memristive Properties and Spike Timing-Dependent Plasticity in Electrodeposited Copper Tungstates and Molybdates

Memristors possess non-volatile memory, adjusting their electrical resistance to the current that flows through them and allowing switching between high and low conducting states. This technology could find applications in fields such as IT, consumer electronics, computing, sensors, and medicine. In this paper, we report successful electrodeposition of thin-film materials consisting of copper tungstate and copper molybdate (CuWO4 and Cu3Mo2O9), which showed notable memristive properties. Material characterisation was performed with techniques such as XRD, XPS, and SEM. The electrodeposited materials exhibited the ability to switch between low and high resistive states during varied cyclic scans and short-term impulses. The retention time of these switched states was also explored. Using these materials, the effects seen in biological systems, specifically spike timing-dependent plasticity, were simulated, being based on analogue operation of the memristors to achieve multiple conductivity states. Bio-inspired simulations performed directly on the material could possibly offer energy and time savings for classical computations. Memristors could be crucial for the advancement of high-efficiency, low-energy neuromorphic electronic devices and technologies in the future.

Emerging Memtransistors for Neuromorphic System Applications: A Review

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1-xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO₄) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO₄ compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

Evaluation of Cellulose–MXene Composite Hydrogel Based Bio-Resistive Random Access Memory Material as Mimics for Biological Synapses

We report a memory device based on organic-inorganic hybrid cellulose-Ti₃C₂T_X MXene composite hydrogel (CMCH) as a switching layer sandwiched between Ag top and FTO bottom electrodes. The device (Ag/CMCH/FTO) was fabricated by a simple, solution-processed route and exhibits reliable and reproducible bipolar resistive switching. Multilevel switching behavior was observed at low operating voltages (±0.5 to ±1 V). Furthermore, the capacitive-coupled memristive characteristics of the device were corroborated with electrochemical impedance spectroscopy and this affirmed the filamentary conduction switching mechanism (LRS-HRS). The synaptic functions of the CMCH-based memory device were evaluated, wherein potentiation/depression properties over 8 × 10³ electric pulses were observed. The device also exhibited spike time-dependent plasticity-based symmetric Hebbian learning rule of a biological synapse. This hybrid hydrogel is expected to be a potential switching material for low-cost, sustainable, and biocompatible memory storage devices and artificial synaptic applications.

Controllable digital and analog resistive switching behavior of 2D layered WSe2 nanosheets for neuromorphic computing

Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe₂ nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 10⁴. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe₂ nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.

Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors

Two-dimensional (2D) materials have become potential resistive switching (RS) layers to prepare emerging non-volatile memristors. The atomically thin thickness and the highly controllable defect density contribute to the construction of ultimately scaled memory cells with stable switching behaviors. Although the conductive bridge random-access memory based on 2D hexagonal boron nitride has been widely studied, the realization of RS completely relying on vacancies in 2D materials has performance superiority. Here, we synthesize carbon-doped h-BN (C-h-BN) with a certain number of defects by controlling the weight percentage of carbon powder in the source. These defects can form a vacancy-based conductive filament under an applied electric field. The memristor displays bipolar non-volatile memory with a low SET voltage of 0.85 V and shows a long retention time of up to 10⁴ s at 120 °C. The response times of the SET and RESET process are less than 80 ns and 240 ns, respectively. The current mapping by conductive atomic force microscopy demonstrates the electric-field-induced current tunneling from defective sites of the C-h-BN flake, revealing the defect-based RS in the C-h-BN memristor. Moreover, C-h-BN with excellent flexibility can be applied to wearable devices, maintaining stable RS performance in a variety of bending environments and after multiple bending cycles. The vacancy-based 2D memristor provides a new strategy for developing ultra-scaled memory units with high controllability.

One-Step Preparation of Semiconductor/Dielectric Bilayer Structures for the Simulation of Flexible Bionic Photonic Synapses

Flexible synaptic devices with information sensing, processing, and storage functions are indispensable in the development of wearable artificial intelligence electronic systems. Here, a semiconductor/dielectric bilayer structure was prepared by a one-step deposition method and used for the first time in a flexible biomimetic photonic synaptic transistor device. Specifically, poly(3-hexylthiophene)-block-poly(phenyl isocyanide) with pentafluorophenyl ester (P3HT-b-PPI(5F)) was prepared as the device active layer, where the P3HT segment served as a carrier transport channel and optical gate and the PPI(5F) segment was used for charge trapping. Various biomimetic synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and short-term/long-term memory, were successfully simulated under green light stimulation. An ultra-low energy consumption of 1.82 fJ was achieved with a greatly reduced operating voltage. Further, the "Morse-code" optical decoding was simulated using the excellent synaptic plasticity of the device. In addition, flexible synaptic devices were prepared by a one-step deposition method and can be well-affixed to arbitrary substrates. This has promising applications in the field of wearable bionic electronics.

The Impact of Trap-Assisted Tunneling and Poole–Frenkel Emission on Synaptic Potentiation in an α-Fe2O3/p-Si Memristive Device

A signature of synaptic potentiation conductance has been observed in an α-Fe2O3/p-Si device fabricated using spin coating. The conductance of the device in dark conditions and illumination with a white light source was characterized as a function of the application of a periodic bias (voltage) with a triangular profile. The conductance of the device increases with the number of voltage cycles applied and plateaus to its maximum value of 0.70 μS under dark conditions and 12.00 μS under illumination, and this mimics the analog synaptic weight change with the action potential of a neuron. In the range of applied voltage from 0 V to 0.7 V, the conduction mechanism corresponds to trap-assisted tunneling (TAT) and in the range of 0.7–5 V it corresponds to the Poole–Frenkel emission (PFE). The conductance as a function of electrical pulses was fitted with a Hill function, which is a measure of cooperation in biological systems. In this case, it allows one to determine the turn-on threshold (K) of the device in terms of the number of voltage pulses, which are found to be 3 and 166 under dark and illumination conditions, respectively. The gradual conductance change and activation after a certain number of pulses perfectly mimics the synaptic potentiation of neurons. In addition, the threshold parameter extracted from the Hill equation fit, acting as the number of pulses for synaptic activation, is found to have programmability with the intensity of the light illumination.

Efficient Memristive Circuit Design of Neural Network-Based Associative Memory for Pavlovian Conditional Reflex

The brain's learning and adaptation processes heavily rely on the concept of associative memory. One of the most basic associative learning processes is classical conditioning. This work presents a memristive neural network-based associative memory system. The system can emulate Pavlovian conditioning principles including acquisition, extension, generalization, differentiation, and spontaneous recovery that have not been considered in most of the previous counterparts. The proposed circuit can emulate these principles thanks to the resistance-changing characteristics of the memristor. Generalization has been achieved by providing both unconditional and neutral stimuli to the network to reduce the memristance of the memristor. Differentiation has been attained by employing unconditional and conditional stimuli in a training scheme to obtain a certain memristance that causes the network to respond differently to both stimuli. A revival of an exterminated stimuli is also done by increasing the synaptic weight of the system. Compared to previous designs, the proposed memristive circuit can implement all the functions of conditional reflex. Our rigorous simulations demonstrated that the proposed memristive system can condition neutral stimuli, show generalization between similar stimuli, distinguish dissimilarities between the generalized stimuli, and recover faded stimuli.

Tunable Resistive Switching in 2D MXene Ti3C2 Nanosheets for Non-Volatile Memory and Neuromorphic Computing

An artificial synapse is essential for neuromorphic computing which has been expected to overcome the bottleneck of the traditional von-Neumann system. Memristors can work as an artificial synapse owing to their tunable non-volatile resistance states which offer the capabilities of information storage, processing, and computing. In this work, memristors based on two-dimensional (2D) MXene Ti₃C₂ nanosheets sandwiched by Pt electrodes are investigated in terms of resistive switching (RS) characteristics, synaptic functions, and neuromorphic computing. Digital and analog RS behaviors are found to coexist depending on the magnitude of operation voltage. Digital RS behaviors with two resistance states possessing a large switching ratio exceeding 10³ can be achieved under a high operation voltage. Analog RS behaviors with a series of resistance states exhibiting a gradual change can be observed at a relatively low operation voltage. Furthermore, artificial synapses can be implemented based on the memristors with the basic synaptic functions, such as long-term plasticity of long-term potentiation and depression and short-term plasticity of the paired-pulse facilitation and depression. Moreover, the "learning-forgetting" experience is successfully emulated based on the artificial synapses. Also, more importantly, the artificial synapses can construct an artificial neural network to implement image recognition. The coexistence of digital and analog RS behaviors in the 2D Ti₃C₂ nanosheets suggests the potential applications in non-volatile memory and neuromorphic computing, which is expected to facilitate simplifying the manufacturing complexity for complex neutral systems where analog and digital switching is essential for information storage and processing.

Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix

The conductive filament (CF) model, as an important means to realize synaptic functions, has received extensive attention and has been the subject of intense research. However, the random and uncontrollable growth of CFs seriously affects the performances of such devices. In this work, we prepared a neural synaptic device based on polyvinyl pyrrolidone-molybdenum disulfide quantum dot (MoS₂ QD) nanocomposites. The doping with MoS₂ QDs was found to control the growth mode of Ag CFs by providing active centers for the formation of Ag clusters, thus reducing the uncertainty surrounding the growth of Ag CFs. As a result, the device, with a low power consumption of 32.8 pJ/event, could be used to emulate a variety of synaptic functions, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, post-tetanic potentiation, short-term memory to long-term memory conversion, and "learning experience" behavior. After having undergone consecutive stimulation with different numbers of pulses, the device stably realized a "multi-level LTP to LTD conversion" function. Moreover, the synaptic characteristics of the device experienced no degradation due to mechanical stress. Finally, the simulation result based on the synaptic characteristics of our devices achieved a high recognition accuracy of 91.77% in learning and inference tests and showed clear digital classification results.

Voltage-Controlled Conversion from CDS to MDS in an Azobenzene-Based Organic Memristor for Information Storage and Logic Operations

For organic memristors, non-zero-crossing current-voltage (I-V) curves are often observed, which can be attributed to capacitive effects. If the conversion between the capacitance-dominated state (CDS) and the memristance-dominated state (MDS) can be realized in a controllable manner, more device functions can be obtained. In this work, a two-terminal memristor using a common organic dye, azobenzene (AZB), as the active layer was prepared. It is found that as the applied voltage gradually increases, the device can transition from CDS to MDS. In the low voltage range (<1 V), the device is in CDS, and the capacitance is significantly increased by ∼10⁴ compared to the theoretical value. In the high voltage range (>1 V), the device is in MDS, achieving an HRS (high resistance state)/LRS (low resistance state) resistance ratio of ∼10⁴, and the logic operations are achieved. Through the analysis of the I-V curve, energy diagram of the materials, and computer simulation results, the mechanisms of CDS, MDS, and their conversion are proposed. This work provides an in-depth understanding of the working mechanism of organic memristors and demonstrates the potential of AZB-based organic memristors for information storage and logic display applications.

Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing

As the emerging member of zero-dimension transition metal dichalcogenide, WSe₂ quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe₂ QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe₂ QDs/La_0.3Sr_0.7MnO₃/SrTiO₃. The device displays excellent resistive switching memory behavior with a R_OFF/R_ON ratio of ~5 × 10³, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe₂ QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

Low-Power Memristor Based on Two-Dimensional Materials

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.

Applications of biomemristors in next generation wearable electronics

With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e. sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.

Emulation of Synaptic Plasticity on a Cobalt-Based Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements─such as synaptic devices and neurons─are crucial. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.

Nanofiber Architecture Engineering Implemented by Electrophoretic-Induced Self-Assembly Deposition Technology for Flash-Type Memristors

Electrophoretic deposition (EPD) has been recognized as a promising large-scale film preparation technology for industrial application. Inspired by the conventional EPD method and the crystal diffusion growth strategy, we propose a modified electrophoretic-induced self-assembly deposition (EPAD) technique to control the morphologies of organic functional materials. Here, an ionic-type dye with a conjugated skeleton and strong noncovalent interactions, celestine blue (CB), is chosen as a module molecule for EPAD investigation. As expected, CB molecules can assemble into different nanostructures, dominated by applied voltage, concentration effect, and duration. Compared to a nanopillar layered packing structure formed by the traditional spin-coating method, the EPAD approach can produce a nanofiber structure under a fixed condition of 10 V/10 min. Intriguingly, a memristor device based on a pillar-like nanostructure exhibits WORM-type behavior, while a device based on nanofibers presents Flash memory performance. The assemble process and the memory mechanism are uncovered by molecular dynamics simulations and density-functional theory (DFT) calculations. This work endows the typical EPD technique with a fresh application scenario, where an in-depth study on the growth mechanism of nanofibers and the positive effect of unique morphologies on memristor performance are offered.

Contribution of Polymers to Electronic Memory Devices and Applications

Electronic memory devices, such as memristors, charge trap memory, and floating-gate memory, have been developed over the last decade. The use of polymers in electronic memory devices enables new opportunities, including easy-to-fabricate processes, mechanical flexibility, and neuromorphic applications. This review revisits recent efforts on polymer-based electronic memory developments. The versatile contributions of polymers for emerging memory devices are classified, providing a timely overview of such unconventional functionalities with a strong emphasis on the merits of polymer utilization. Furthermore, this review discusses the opportunities and challenges of polymer-based memory devices with respect to their device performance and stability for practical applications.