Understanding memristive switching via in situ characterization and device modeling

Artificial synapses based on HfOx/TiOymemristor devices for neuromorphic applications

As a result of enormous progress in nanoscale electronics, interest in artificial intelligence (AI) supported systems has also increased greatly. These systems are typically designed to process computationally intensive data. Parallel processing neural network architectures are particularly noteworthy for their ability to process dense data at high speeds, making them suitable candidates for AI algorithms. Due to their ability to combine processing and memory functions in a single device, memristors offer a significant advantage over other electronic platforms in terms of area scaling efficiency and energy savings. In this study, single-layer and bilayer metal-oxide HfOxand TiOymemristor devices inspired by biological synapses were fabricated by pulsed laser and magnetron sputtering deposition techniques in high vacuum with different oxide thicknesses. The structural and electrical properties of the fabricated devices were analysed using x-ray reflectivity, x-ray photoelectron spectroscopy, and standard two-probe electrical characterization measurements. The stoichiometry and degree of oxidation of the elements in the oxide material for each thin film were determined. Moreover, the switching characteristics of the metal oxide upper layer in bilayer devices indicated its potential as a selective layer for synapse. The devices successfully maintained the previous conductivity values, and the conductivity increased after each pulse and reached its maximum value. Furthermore, the study successfully observed synaptic behaviours with long-term potentiation, long-term depression (LTD), paired-pulse facilitation, and spike-timing-dependent plasticity, showcasing potential of the devices for neuromorphic computing applications.

Reliable and Robust Two-Dimensional Perovskite Memristors for Flexible-Resistive Random-Access Memory Array

Two-dimensional (2D) halide perovskites have become a promising class of memristive materials due to their low power consumption, compositional versatility, and microstructural anisotropy in electronics. However, implementing high-performance resistive random-access memory requires a higher reliability and moisture resistance. To address these issues, component studies and attempts to improve the phase stability have been reported but have not been able to achieve sufficient reliability. Here, highly textured thin films grown perpendicular to the substrate in Ruddlesden-Popper 2D perovskites exhibited highly stable and reliable binary memory performance. We further built a flexible crossbar array to verify data storage capability, achieving a high device yield, robust endurance, long retention, reliability to operate under bending conditions, and moisture stability over a year. These device performances are attributed to preformed vertically oriented nanocrystals that allow the conductive filaments to operate reliably. Our finding provides the material design strategy that can be extended to the development of semiconductor materials for next-generation memory devices.

Conductive filament distribution in nano-scale electrochemical metallization cells

We report a combined experimental and theoretical study of the spatial distributions and sizes of conductive filaments in nano-scale electrochemical metallization (ECM) cells. Each cell comprises a silver nanocube as active electrode, a titanium dioxide (TiO2) or aluminum oxide (Al2O3) layer as dielectric, and a highly-doped silicon substrate as passive counter electrode. Following electroforming of the ECM cell and subsequent mechanical delamination of the silver nanocubes, current maps at previous particle locations reveal an intriguing metal distribution in the TiO2, with preferential accumulation close to the original locations of the nanocube edges. We assign this behavior to electric field enhancements close to the cube edge positions. In contrast, filaments in Al2O3 layers show a comparatively homogenous distribution, which may be assigned to its lower dielectric permittivity. By increasing the oxide thickness, the total area of conductive spots in the current maps increases monotonically for both materials. Kinetic Monte-Carlo simulations of ion migration dynamics in TiO2 confirm the experimental observations, describing both the preferred locations and oxide thickness-dependent metal loadings associated with filament formation. Overall, our findings are highly valuable for the design of future electrochemical metallization cells, especially in the sub-100 nm regime, where optimal filament control is of major importance for achieving lowest device-to-device variability.

Nanoscale Operando Imaging of Electrically Driven Charge-Density Wave Phase Transitions

Structural transformations in strongly correlated materials promise efficient and fast control of materials' properties via electrical or optical stimulation. The desired functionality of devices operating based on phase transitions, however, will also be influenced by nanoscale heterogeneity. Experimentally characterizing the relationship between microstructure and phase switching remains challenging, as nanometer resolution and high sensitivity to subtle structural modifications are required. Here, we demonstrate nanoimaging of a current-induced phase transformation in the charge-density wave (CDW) material 1T-TaS2. Combining electrical characterizations with tailored contrast enhancement, we correlate macroscopic resistance changes with the nanoscale nucleation and growth of CDW phase domains. In particular, we locally determine the transformation barrier in the presence of dislocations and strain, underlining their non-negligible impact on future functional devices. Thereby, our results demonstrate the merit of tailored contrast enhancement and beam shaping for advanced operando microscopy of quantum materials and devices.

Hierarchical self-assembly of Au-nanoparticles into filaments: evolution and break

We compare the assembly of individual Au nanoparticles in a vacuum and between two Au(111) surfaces via classical molecular dynamics on a timescale of 100 ns. In a vacuum, the assembly of three nanoparticles used as seeds, initially showing decahedral, truncated octahedral and icosahedral shapes with a diameter of 1.5-1.7 nm, evolves into a spherical object with about 10-12 layers and a gyration radius ∼2.5-2.8 nm. In a vacuum, 42% show just one 5-fold symmetry axis, 33% adopt a defected icosahedral arrangement, and 25% lose all 5-fold symmetry and display a face-centred-cubic shape with several parallel stacking faults. We model a constrained version of the same assembly that takes place between two Au(111) surfaces. During the dynamics, the two Au(111) surfaces are kept fixed at distances of 55 Å, 55.5 Å, 56 Å, and 56.5 Å. The latter distance accommodates 24 Au layers with no strain, while the others correspond to nominal strains of 1.5%, 2.4%, and 3.3%, respectively. In the constrained assembly, each individual seed tends to reorganize into a layered configuration, but the filament may break. The probability of breaking the assembled nanofilament depends on the individual morphology of the seeds. It is more likely to break at the decahedron/icosahedron interface, whilst it is more likely to layer with respect to the (111) orientation when a truncated octahedron sits between the decahedron and the icosahedron. We further observe that nanofilaments between surfaces at 56 Å have a >90% probability of breaking, which decreases to 8% when the surfaces are 55 Å apart. We attribute the dramatic change in probability of breaking to the peculiar decahedron/icosahedron interface and the higher average atomic strain in the nanofilaments. This in silico experiment can shed light on the understanding and control of the formation of metallic nanowires and nanoparticle-assembled networks, which find applications in next-generation electronic devices, such as resistive random access memories and neuromorphic devices.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

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.

Electric-field induced phase separation and dielectric breakdown in leaky dielectric mixtures: Thermodynamics and kinetics

Dielectric materials form the foundation of many electronic devices. When connected to a circuit, these materials undergo changes in microscopic morphology, such as the demixing of dielectric mixtures through phase separation and dielectric breakdown, resulting in the formation of micro-filaments. Consequently, the macroscopic properties and lifespan of the devices are significantly altered. To comprehend the physical mechanisms behind it, we conducted a systematic investigation of the thermodynamics of multicomponent leaky dielectric materials. Beginning with the total energy functional, we derived expressions for the binodal composition, spinodal composition, and critical points. Furthermore, we constructed and validated theoretical phase diagrams for the binary leaky dielectric mixture, incorporating three crucial freedoms: composition, temperature, and electric field strength. In addition, we analyzed the equilibrium interfacial tension impacted by the electric field and studied the dynamic aspects of dielectric materials, examining two morphological transformations: electrostriction and dielectric breakdowns. Our analysis unveiled a connection between these dynamic phenomena and the electric field-induced interfacial instability. The present work is expected to be supportive of future research on multicomponent dielectric materials by offering a comprehensive understanding of their thermodynamic and kinetic behaviors.

Nanofluidic Ionic Memristors

Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

Memristive Control of Plasmon-Mediated Nonlinear Photoluminescence in Au Nanowires

Nonlinear photoluminescence (N-PL) is a broadband photon emission arising from a nonequilibrium heated electron distribution generated at the surface of metallic nanostructures by ultrafast pulsed laser illumination. N-PL is sensitive to surface morphology, local electromagnetic field strength, and electronic band structure, making it relevant to probe optically excited nanoscale plasmonic systems. It also has been key to accessing the complex multiscale time dynamics ruling electron thermalization. Here, we show that plasmon-mediated N-PL emitted by a gold nanowire can be modified by an electrical architecture featuring a nanogap. Upon voltage activation, we observe that N-PL becomes dependent on the electrical transport dynamics and can thus be locally modulated. This finding brings an electrical leverage to externally control the photoluminescence generated from metal nanostructures and constitutes an asset for the development of emerging nanoscale interface devices managing photons and electrons.

Measuring Electrical Resistivity at the Nanoscale in Phase-Change Materials

Electrical resistivity is the key parameter in the active regions of many current nanoscale devices, from memristors to resistive random-access memory and phase-change memories. The local resistivity of the materials is engineered on the nanoscale to fit the performance requirements. Phase-change memories, for example, rely on materials whose electrical resistance increases dramatically with a change from a crystalline to an amorphous phase. Electrical characterization methods have been developed to measure the response of individual devices, but they cannot map the local resistance across the active area. Here, we propose a method based on operando electron holography to determine the local resistance within working devices. Upon switching the device, we show that electrical resistance is inhomogeneous on the scale of only a few nanometers.

Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO2-MoS2 Core-Shell Heterostructures for Future Bio-Inspired Computing

Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.

Reconfigurable memlogic long wave infrared sensing with superconductors

Optical sensors with in-cell logic and memory capabilities offer new horizons in realizing machine vision beyond von Neumann architectures and have been attempted with two-dimensional materials, memristive oxides, phase-changing materials etc. Noting the unparalleled performance of superconductors with both quantum-limited optical sensitivities and ultra-wide spectrum coverage, here we report a superconducting memlogic long-wave infrared sensor based on the bistability in hysteretic superconductor-normal phase transition. Driven cooperatively by electrical and optical pulses, the device offers deterministic in-sensor switching between resistive and superconducting (hence dissipationless) states with persistence > 105 s. This results in a resilient reconfigurable memlogic system applicable for, e.g., encrypted communications. Besides, a high infrared sensitivity at 12.2 μm is achieved through its in-situ metamaterial perfect absorber design. Our work opens the avenue to realize all-in-one superconducting memlogic sensors, surpassing biological retina capabilities in both sensitivity and wavelength, and presents a groundbreaking opportunity to integrate visional perception capabilities into superconductor-based intelligent quantum machines.

In Situ Unveiling of the Resistive Switching Mechanism of Halide Perovskite-Based Memristors

The organic-inorganic halide perovskite has become one of the most promising candidates for next-generation memory devices, i.e. memristors, with excellent performance and solution-processable preparation. Yet, the mechanism of resistive switching in perovskite-based memristors remains ambiguous due to a lack of in situ visualized characterization methods. Here, we directly observe the switching process of perovskite memristors with in situ photoluminescence (PL) imaging microscopy under an external electric field. Furthermore, the corresponding element composition of conductive filaments (CFs) is studied, indicating that the metallic CFs with respect to the activity of the top electrode are essential for device performance. Finally, electrochemical impedance spectroscopy (EIS) is conducted to reveal that the transition of ion states is associated with the formation of metallic CFs. This study provides in-depth insights into the switching mechanism of perovskite memristors, paving a pathway to develop and optimize high-performance perovskite memristors for large-scale applications.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.

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

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

High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the limited tuning speed and large power consumption of the phase shifters used. In this paper, we introduce the memresonator, a metal-oxide memristor heterogeneously integrated with a microring resonator, as a non-volatile silicon photonic phase shifter. These devices are capable of retention times of 12 hours, switching voltages lower than 5 V, and an endurance of 1000 switching cycles. Also, these memresonators have been switched using 300 ps long voltage pulses with a record low switching energy of 0.15 pJ. Furthermore, these memresonators are fabricated on a heterogeneous III-V-on-Si platform capable of integrating a rich family of active and passive optoelectronic devices directly on-chip to enable in-memory photonic computing and further advance the scalability of integrated photonic processors.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Coherent Phonon Manipulation via Electron-Phonon Interaction for Facilitated Relaxation of Metastable Centers in ZnO

Methods that allow versatile manipulation of metastable centers in semiconductors are highly important owing to their potential for quantum information processing and computations. In this study, we demonstrate that the electron-phonon interaction enables phonon participation to promote relaxation of metastable centers in ZnO, which is known for its persistent photoconductivity (PPC) effect. Experimentally, we show that continuous infrared (IR) radiation (1064 nm, ∼30 mW/cm2) promotes longitudinal optical phonons via the Fröhlich interaction and increases the PPC relaxation rate by ∼4 folds. More importantly, we discover that coherent phonons activated by an ultrashort pulse IR laser of the same power increased the relaxation rate by ∼1200-fold, as confirmed by ultrafast transient spectroscopy to be correlated to the excitation of coherent acoustic phonons via the inverse piezoelectric effect. We expect this study to provide valuable guidance for the development of novel quantum and photoactive devices.

Gamma radiation-induced nanodefects in diffusive memristors and artificial neurons

Gamma photons with an average energy of 1.25 MeV are well-known to generate large amounts of defects in semiconductor electronic devices. Here we investigate the novel effect of gamma radiation on diffusive memristors based on metallic silver nanoparticles dispersed in a dielectric matrix of silica. Our experimental findings show that after exposure to radiation, the memristors and artificial neurons made of them demonstrate much better performance in terms of stable volatile resistive switching and higher spiking frequencies, respectively, compared to the pristine samples. At the same time we observe partial oxidation of silver and reduction of silicon within the switching silica layer. We propose nanoinclusions of reduced silicon distributed across the silica layer to be the backbone for metallic nanoparticles to form conductive filaments, as supported by our theoretical simulations of radiation-induced changes in the diffusion process. Our findings propose a new opportunity to engineer the required characteristics of diffusive memristors in order to emulate biological neurons and develop bio-inspired computational technology.

Open-loop analog programmable electrochemical memory array

Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.

Solvent-assisted sulfur vacancy engineering method in MoS2 for a neuromorphic synaptic memristor

Recently, two-dimensional transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS2) have attracted great attention due to their unique properties. To modulate the electronic properties and structure of TMDs, it is crucial to precisely control chalcogenide vacancies and several methods have already been suggested. However, they have several limitations such as plasma damage by ion bombardment. Herein, we introduced a novel solvent-assisted vacancy engineering (SAVE) method to modulate sulfur vacancies in MoS2. Considering polarity and the Hansen solubility parameter (HSP), three solvents were selected. Sulfur vacancies can be modulated by immersing MoS2 in each solvent, supported by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy analyses. The SAVE method can further expand its application in memory devices representing memristive performance and synaptic behaviors. We represented the charge transport mechanism of sulfur vacancy migration in MoS2. The non-destructive, scalable, and novel SAVE method controlling sulfur vacancies is expected to be a guideline for constructing a vacancy engineering system of TMDs.

Tuning Electrochemical Stability of 5,10-Ditolylphenazine-Based Antiaromatic Materials for Unipolar Memristor toward Artificial Synapses Application

Three organic conjugated small molecules, DTA-DTPZ, Cz-DTPZ, and DTA-me-DTPZ comprising an antiaromatic 5,10-ditolylphenazine (DTPZ) core and electron-donating peripheral substituents with high HOMOs (-4.2 to -4.7 eV) and multiple reversible oxidative potentials are reported. The corresponding films sandwiched between two electrodes show unipolar and switchable hysteresis current-voltage (I-V) characteristics upon voltage sweeping, revealing the prominent features of nonvolatile memristor behaviors. The numerical simulation of the I-V curves suggests that the carriers generated by the oxidized molecules lead to the increment of conductance. However, the accumulated carriers tend to deteriorate the device endurance. The electroactive sites are fully blocked in the dimethylated molecule DTA-me-DTPZ, preventing the irreversible electrochemical reaction, thereby boosting the endurance of the memristor device over 300 cycles. Despite the considerable improvement in endurance, the decrement of on/off ratio from 105 to 101 after 250 cycles suggests that the excessive charge carriers (radical cations) remains a problem. Thus, a new strategy of doping an electron-deficient material, CN-T2T, into the unipolar active layer was introduced to further improve the device stability. The device containing DTA-me-DTPZ:CNT2T (1:1) blend as the active layer retained the endurance and on/off ratio (∼104) upon sweeping 300 cycles. The molecular designs and doping strategy demonstrate effective approaches toward more stable metal-free organic conjugated small-molecule memristors.

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

The analog resistive switching properties of amorphous InGaZnOx (a-IGZO)-based devices with Al as the top and bottom electrodes and an Al-Ox interface layer inserted on the bottom electrode are presented here. The influence of the electrode deposition rate on the surface roughness was established and proposed as the cause of the observed unusual anomalous switching effects. The DC electrical characterization of the optimized Al/a-IGZO/AlOx/Al devices revealed an analog resistive switching with a satisfactory value for retention levels, but the endurance was found to decrease after 200 cycles. The predominant conduction mechanism in these devices was found to be thermionic emission. An in-depth analysis was performed to explore the relaxation kinetics of the device and it was found that the current has a lower decay rate. The current level stability was tested and found reliable even after 5 h. The cost-effective and precious metal-free nature of the a-IGZO memristor investigated in this study makes it a highly desirable candidate for neuromorphic computing applications.

A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses

Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Two-Terminal MoS2 Memristor and the Homogeneous Integration with a MoS2 Transistor for Neural Networks

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS₂ memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS₂ transistors to realize one-transistor-one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.

Vertically integrated spiking cone photoreceptor arrays for color perception

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

Computational Study on Filament Growth Dynamics in Microstructure-Controlled Storage Media of Resistive Switching Memories

The filament growth processes, crucial to the performance of nanodevices like resistive switching memories, have been widely investigated to realize the device optimization. With the combination of kinetic Monte Carlo (KMC) simulations and the restrictive percolation model, three different growth modes in electrochemical metallization (ECM) cells were dynamically reproduced, and an important parameter, the relative nucleation distance, was theoretically defined to measure different growth modes quantitatively; hence their transition can be well described. In our KMC simulations, the inhomogeneity of storage medium is realized through introducing evolutionary void versus non-void sites within it to mimic the real nucleation during filament growth. Finally, the renormalization group method was used in the percolation model to analytically illustrate void-concentration-dependent growth mode transition, fitting KMC simulation results quite well. Our study found that the nanostructure of the medium can dominate the filament growth dynamics, as the simulation images as well as the analytical results are consistent with experiments results. Our study spotlights a vital and intrinsic factor, void concentration (relative to defects, grains, or nanopores) of a storage medium, in inducing filament growth mode transition within ECM cells. This theoretically proves a mechanism to tune performance of ECM systems that controlling microstructures of the storage media can dominate the filament growth dynamics, indicating an accessible strategy, nanostructure processing, for device optimization of ECM memristors.

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

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

Three-Dimensional Reconstruction of Conductive Filaments in HfOx -Based Memristor

HfO_x -based memristor has been studied extensively as one of the most promising memories for the excellent nonvolatile data storage and computing-in-memory capabilities. However, the resistive switching mechanism, relying on the formation and rupture of conductive filaments (CFs) during device operations, is still under debate. In this work, the CFs with different morphologies after different operations-forming, set, and reset-are clearly revealed for the first time by 3D reconstruction of conductive atomic force microscopy (c-AFM) images. Intriguingly, multiple CFs are successfully observed in HfO_x -based memristor devices with three different resistive states. CFs after forming, set, and reset exhibit the typical morphologies of hourglass, inverted-cone, and short-cone, respectively. The rupture location of CFs after the reset operation is also observed clearly. These findings reveal the microscopic behaviors underlying the resistive switching, which could pave the road to design and optimize oxide-based memristors for both memory and computing applications.

Laser printed microelectronics

Printed organic and inorganic electronics continue to be of large interest for sensors, bioelectronics, and security applications. Many printing techniques have been investigated, albeit often with typical minimum feature sizes in the tens of micrometer range and requiring post-processing procedures at elevated temperatures to enhance the performance of functional materials. Herein, we introduce laser printing with three different inks, for the semiconductor ZnO and the metals Pt and Ag, as a facile process for fabricating printed functional electronic devices with minimum feature sizes below 1 µm. The ZnO printing is based on laser-induced hydrothermal synthesis. Importantly, no sintering of any sort needs to be performed after laser printing for any of the three materials. To demonstrate the versatility of our approach, we show functional diodes, memristors, and a physically unclonable function based on a 6 × 6 memristor crossbar architecture. In addition, we realize functional transistors by combining laser printing and inkjet printing.

Exploring the direction-dependency of conductive filament formation and oxygen vacancy migration behaviors in HfO2-based RRAM

Oxygen vacancy (V_O) defects play an essential role in governing the conductivity of semiconductor materials. The direction-dependency of oxygen vacancy conductive filament (CF) formation and V_O migration behaviors in HfO₂-based resistive random access memory (RRAM) were systematically investigated through first-principles calculations. The energetic and electronic structural analyses indicate that the continuous distribution of 3-fold oxygen vacancy (V_O3) or 4-fold oxygen vacancy (V_O4) is more favorable for the CF formation along [010] and [001] directions, and a continuous distribution between V_O3 and V_O4 in the m-HfO₂ system can also combine to promote the formation of CFs along a particular direction. Furthermore, the high annealing temperature and low oxygen partial pressure (P_O2) could effectively reduce the V_O formation energy and promote the formation of CFs, resulting in a lower applied voltage of the devices. Our results indicate that q = 0 and q = +2 are the most probable charge states for V_O3 and V_O4 in m-HfO₂. Subsequently, it is found that the low activation energy of V_O originates from the +2q charged V_O3 or V_O4 migrating in the CFs along a particular crystallographic [001] direction. The diffusion coefficient (D) of the oxygen atom along the [001] direction is much higher than that of all the other possible pathways considered, due to the lower energy barrier. This demonstrates that the growth of CFs is potentially direction-dependent, and that a lower forming voltage and lower SET voltage are required when the CFs are grown along a particular direction in RRAM devices. The present work would help to provide a fundamental guide and new understanding for the development and application of HfO₂-based RRAM.

Functional Fiber Materials to Smart Fiber Devices

The development of fiber materials has accompanied the evolution of human civilization for centuries. Recent advances in materials science and chemistry offered fibers new applications with various functions, including energy harvesting, energy storing, displaying, health monitoring and treating, and computing. The unique one-dimensional shape of fiber devices endows them advantages to work as human-interfaced electronics due to the small size, lightweight, flexibility, and feasibility for integration into large-scale textile systems. In this review, we first present a discussion of the basics of fiber materials and the design principles of fiber devices, followed by a comprehensive analysis on recently developed fiber devices. Finally, we provide the current challenges facing this field and give an outlook on future research directions. With novel fiber devices and new applications continuing to be discovered after two decades of research, we envision that new fiber devices could have an important impact on our life in the near future.

Emerging memristive neurons for neuromorphic computing and sensing

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

A Monolithic Stochastic Computing Architecture for Energy Efficient Arithmetic

As the energy and hardware investments necessary for conventional high-precision digital computing continue to explode in the era of artificial intelligence (AI), a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since, unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting, etc., SC can implement the same using simple logic gates. While it is possible to accelerate SC using traditional silicon complementary metal-oxide-semiconductor (CMOS) technology, the need for extensive hardware investment to generate stochastic bits (s-bits), the fundamental computing primitive for SC, makes it less attractive. Memristor and spin-based devices offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here, the limitations of existing and emerging technologies are overcome, and a standalone SC architecture embedded in memory and based on 2D memtransistors is experimentally demonstrated. The monolithic and non-von-Neumann SC architecture occupies a small hardware footprint and consumes a miniscule amount of energy (<1 nJ) for both s-bit generation and arithmetic operations, highlighting the benefits of SC.

Metal Halide Perovskite/Electrode Contacts in Charge-Transporting-Layer-Free Devices

Metal halide perovskites have drawn substantial interest in optoelectronic devices in the past decade. Perovskite/electrode contacts are crucial for constructing high-performance charge-transporting-layer-free perovskite devices, such as solar cells, field-effect transistors, artificial synapses, memories, etc. Many studies have evidenced that the perovskite layer can directly contact the electrodes, showing abundant physicochemical, electronic, and photoelectric properties in charge-transporting-layer-free perovskite devices. Meanwhile, for perovskite/metal contacts, some critical interfacial physical and chemical processes are reported, including band bending, interface dipoles, metal halogenation, and perovskite decomposition induced by metal electrodes. Thus, a systematic summary of the role of metal halide perovskite/electrode contacts on device performance is essential. This review summarizes and discusses charge carrier dynamics, electronic band engineering, electrode corrosion, electrochemical metallization and dissolution, perovskite decomposition, and interface engineering in perovskite/electrode contacts-based electronic devices for a comprehensive understanding of the contacts. The physicochemical, electronic, and morphological properties of various perovskite/electrode contacts, as well as relevant engineering techniques, are presented. Finally, the current challenges are analyzed, and appropriate recommendations are put forward. It can be expected that further research will lead to significant breakthroughs in their application and promote reforms and innovations in future solid-state physics and materials science.

Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices

Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.

Resistance Switching in Polycrystalline C12A7 Electride

The memory (memristive) properties of an electride material based on polycrystalline mayenite (C12A7:e-) were studied. The phase composition of the material has been confirmed by such methods as XRD, TEM, Raman, and infrared spectroscopy. The electride state was confirmed by conductivity measurements and EPR using a characteristic signal from F+-like centers, but the peak at 186 cm-1, corresponding to an electride with free electrons, was not observed explicitly in the Raman spectra. The temperature dependence of current-voltage characteristics in states with low and high resistance (LRS and HRS) has been studied. In the LRS state, the temperature dependence of the current has a non-Arrhenius character and is described by the Hurd quantum tunnelling model with a Berthelot temperature of 262 K, while in the HRS state, it can be described in terms of the Arrhenius model. In the latter case, the existence of two conduction regions, "impurity" and "intrinsic", with corresponding activation energies of 25.5 and 40.6 meV, was assumed. The difference in conduction mechanisms is most likely associated with a change in the concentration of free electrons.

Memristor Degradation Analysis Using Auxiliary Volt-Ampere Characteristics

The memristor is one of the modern microelectronics key devices. Due to the nanometer scale and complex processes physic, the development of memristor state study approaches faces limitations of classical methods to observe the processes. We propose a new approach to investigate the degradation of six Ni/Si₃N₄/p+Si-based memristors up to their failure. The basis of the proposed idea is the joint analysis of resistance change curves with the volt-ampere characteristics registered by the auxiliary signal. The paper considers the existence of stable switching regions of the high-resistance state and their interpretation as stable states in which the device evolves. The stable regions' volt-ampere characteristics were simulated using a compact mobility modification model and a first-presented target function to solve the optimization problem.

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

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

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO_x ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 × 25 TiO_x memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiO_x memristor performance such as threshold uniformity (≈2.7%), device yield (> 99%), repetitive stability (≈3000 spikes), low asymmetry value of ≈1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at ≈95.2% of classification accuracy.

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.

Synaptic Plasticity and Quantized Conductance States in TiN-Nanoparticles-Based Memristor for Neuromorphic System

Controlled conductive filament formation in the resistive random access memory device is an essential requirement for analog resistive switching to develop artificial synapses. In this work, we have studied Au/Ti/HfAlOx/TiN-NP/HfAlOx/ITO RRAM device to demonstrate conductance quantization behavior to achieve the high-density memory application. Stepwise change in conductance under DC and pulse voltage confirms the quantized conductance states with integer and half-integer multiples of G0. Reactive TiN-NPs inside the switching layer helps to form and rupture the atomic scale conductive filaments due to enhancing the local electric field inside. Bipolar resistive switching characteristics at low SET/RESET voltage were obtained with memory window > 10 and stable endurance of 103 cycles. Short-term and long-term plasticities are successfully demonstrated by modulating the pre-spike number, magnitude, and frequency. The quantized conductance behavior with promising synaptic properties obtained in the experiments suggests HfAlOx/TiN-NP/HfAlOx switching layer is suitable for multilevel high-density storage RRAM devices.

Toward memristive in-memory computing: principles and applications

With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have been demonstrated and verified. These applications can be broadly categorized into two major types: soft computing that can tolerant uncertain and imprecise results, and hard computing that emphasizes explicit and precise numerical results for each task, leading to different requirements on the computational accuracies and the corresponding hardware solutions. In this review, we conduct a thorough survey of the recent advances of memristive in-memory computing applications, both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms, and the hard computing type that includes scientific computing and digital image processing. At the end of the review, we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.

Reliable Memristor Based on Ultrathin Native Silicon Oxide

Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiO_x (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiO_x enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.

A Bioinspired Artificial Injury Response System Based on a Robust Polymer Memristor to Mimic a Sense of Pain, Sign of Injury, and Healing

Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human-robot interactions. However, more advanced capabilities, such as human-like self-protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK-800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK-800/Pt have outstanding switching endurance >106  cycles, orders of magnitude higher than any other two-terminal polymer/organic memristors in literature (typically 102 -103 cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK-800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK-800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated.