Oxide Memristors Research Articles

MemriSim: A theoretical framework for simulating electron transport in oxide memristors

We have developed a theoretical framework MemriSim for simulating the resistive switching behaviors of oxide memristors. MemriSim comprises two major parts, i) structural evolution of oxygen vacancies during conductive filament formation/rupture by kinetic Monte Carlo (kMC) algorithm, and ii) transport calculations based on the scenario of electron tunneling and thermionic emission with the kMC derived structures. As prototype probes, we have computed the current-voltage (I-V) curves of HfO2 and TaOx based memristors and compared the results with experimental measurements, which show perfect agreement. By tuning the physical parameters, MemriSim can describe resistive switching devices with different oxide layers and metal electrodes. In addition, the pulse transient current can also be simulated by considering the transient response of RLC circuit. The developed framework not only provides a general approach for understanding the fundamental mechanism of resistive switching in oxides, but also opens up new opportunities for designing and optimizing memristor-based architectures for nonvolatile memory, logic-in-memory and neuromorphic computing.Program summaryProgram Title: MemriSim.CPC Library link to program files: https://doi.org/10.17632/8gbbgf8z49.1Licensing provisions: GPLv2.Programming language: C++.Supplementary material: Supplementary material is available.Nature of problem: A general framework for simulating the resistive switching properties of oxide-based memristors; generate the structure of oxide layer during filament formation/rupture; calculate the I-V curves of memristive device; simulate the pulse transient current; predict the resistive switching performance of new devices.Solution method: The framework uses kMC algorithm for structural evolution, the electric field inside oxide layer is computed by the Poisson's equation, and the transport calculation is based on electron tunneling and thermionic emission.

Enhancing reliability in oxide-based memristors using two-dimensional transition metal dichalcogenides

Oxide-based memristor is an attractive candidate for future nonvolatile resistive random access memory (RRAM) devices. However, it suffers from insufficient reliability, owing to the randomness of the conductive filaments, hindering the practical use of the memristor for future RRAM applications. Here, we propose harnessing the two-dimensional (2D) transition metal dichalcogenides (TMDs) on oxide memristor to achieve high device reliability by controlling oxygen vacancy-based filaments near the TMDs/oxide interface. By forming the Pt/WSe2/HfxZr1-xO2 (HZO)/TiN structure, the fabricated memristor exhibits high reliability with good cyclic endurance (over 2,000 cycles), retention (104 s), and low cycle-to-cycle variability. Surface chemical analysis reveals the abundant oxygen vacancies induced by forming WSe2/HZO interface are the source of filamentary switching. By incorporating 2D materials and oxides, the practical application of memristor to future information processing devices can be boosted by the enhanced device reliability.

Perspective: Entropy-stabilized oxide memristors

A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

Synaptic plasticity in zinc oxide-based flexible invisible transparent memristor by modulating oxygen concentration

Artificial synapses based on memristors are used in emulating the synaptic plasticity behavior of a human brain. Here, we have proposed a transparent memristor based on aluminum zinc oxide (AZO) on a flexible substrate—polyethylene naphthalate. We have analyzed the elemental composition of the gadget subjected to the optimized flow rate of Ar/O2 = 2/1 by x-ray photoelectron spectroscopy. The prepared AZO/ZnO/indium-doped tin oxide memristor exhibits a bipolar switching behavior with Vset/Vreset of 1.4/−2.0 V. The results reflect an acceptable endurance of >500 cycles and retention of 104 s. The optimized device shows an improvement in the non-linearity of potentiation—2.31/depression—3.05 and has more than 25 cycles of stability. The transparency is checked using a UV-visible spectrophotometer showing 90% transparency in the visible region making the device suitable for applications in invisible electronics. Our results reflect that the proposed device can be used as a transparent electrode in making artificial synapses for neuromorphic applications.

Volatile tin oxide memristor for neuromorphic computing

The rise of neuromorphic systems has addressed the shortcomings of current computing architectures, especially regarding energy efficiency and scalability. These systems use cutting-edge technologies such as Pt/SnOx/TiN memristors, which efficiently mimic synaptic behavior and provide potential solutions to modern computing challenges. Moreover, their unipolar resistive switching ability enables precise modulation of the synaptic weights, facilitating energy-efficient parallel processing that is similar to biological synapses. Additionally, memristors' spike-rate-dependent plasticity enhances the adaptability of neural circuits, offering promising applications in intelligent computing. Integrating memristors into edge computing architectures further highlights their importance in tackling the security and efficiency issues associated with conventional cloud computing models.

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.

Enhanced Computational Study with Experimental Correlation on I-V Characteristics of Tantalum Oxide (TaOx) Memristor Devices in a 1T1R Configuration.

Memristors, non-volatile switching memory platform, has recently attracted significant interest, offering unique potential to enable the realization of human brain-like neuromorphic computing efficiency. Memristors also demonstrate excellent temperature tolerance, long-term durability, and high tunability with nanosecond pulses, making them highly attractive for neuromorphic computing applications. To better understand the material processing, microstructure, and property relationship of switching mechanisms in memristor devices, computational methodologies, and tools are developed to predict the I-V characteristics of memristor devices based on tantalum oxide (TaOx) resistive random-access memory (ReRAM) integrated with an n-channel metal-oxide-semiconductor (NMOS) transistor. A multiphysics model based on coupled partial differential equations for electrical and thermal transport phenomena is solved for the high- and low-resistance states during the formation, growth, and destruction of a conducting filament through SET and RESET stages. These stages effectively represent the migration of oxygen vacancies within an oxide exchange layer. A series of parametric studies and energy minimization calculations are conducted to determine probable ranges for key material and model parameters accounting for the experimental data. The computational model successfully predicted the measured I-V curves across various gate voltages applied to the NMOS transistor in the one transistor one resistance (1T1R) configuration.

Fabrication of high performance memristor device by metallization of Ag+ inside a solution processed Li5AlO4 thin film

A solution processed Ag-ion-exchanged Li5AlO4 thin film has been used to fabricate a high performance oxide memristor device with enhanced bistable switching and memory retention. For this device fabrication, Li5AlO4...

On-receptor computing with classical associative learning in semiconductor oxide memristors.

The increasing demand for energy-efficient data processing leads to a growing interest in neuromorphic computing that aims to emulate cerebral functions. This approach offers cost-effective and rapid parallel data processing, surpassing the limitations of the conventional von Neumann architecture. Key to this emulation is the development of memristors that mimic biological synapses. Recently, research efforts have focused on the incorporation of nociceptors-sensory neurons capable of detecting external stimuli-into memristors for applications in robotics and artificial intelligence. This integration enables memristors to adapt to various circumstances while remaining cost-effective. A nonfilamentary gradual resistive switching memristor is utilized to implement artificial nociceptor and synaptic behaviors. The fabricated Pt/indium gallium zinc oxide (IGZO)/SnOx/TiN device exhibits essential properties of biological nociceptors, including threshold response, no-adaptation, relaxation, sensitization, and recovery. Furthermore, the device leverages short-term memory principles to emulate learning behaviors observed in the brain by showcasing "forgetting" paradigms. Additionally, control of the input spikes yields different synaptic plasticity responses, thus emulating the key functions of our synapse. Computational simulations demonstrate the device's ability to perform both computing and sensing tasks effectively, thus enabling on-receptor computing with associative learning capabilities.

Correlation between oxygen flow-controlled resistive switching and capacitance behavior in gallium oxide memristors grown via RF sputtering

We studied on the bipolar resistive switching (RS)-dependent capacitance of Ga2O3 memristors, grown using controlled oxygen flow via a radio frequency sputtering process. The Ag/Ga2O3/Pt memristor structure was employed to investigate the capacitance changes associated with RS behavior and oxygen concentration. In the low-resistance state (LRS), capacitance increased by over 60 times compared to the high-resistance state (HRS). Furthermore, in the HRS state, increasing the oxygen flow from 0 to 0.3 sccm resulted in an 80 % decrease in capacitance, while in the LRS state, capacitance increased by 128 %. These results indicate that RS-dependent capacitance in Ga2O3 memristors is influenced by the density of oxygen vacancies. The presence of oxygen vacancies affects charge storage capacity and capacitance, with higher oxygen concentrations leading to reduced capacitance in HRS and increased capacitance in LRS. The results contribute to the understanding of the capacitance behavior in Ga2O3 memristors and highlight the significance of oxygen vacancies in their operation.

Direct Visualization of Charge Migration in Bilayer Tantalum Oxide Films by Multimodal Imaging

AbstractInspired by biological neuromorphic computing, artificial neural networks based on crossbar arrays of bilayer tantalum oxide memristors have shown to be promising alternatives to conventional complementary metal‐oxide‐semiconductor (CMOS) architectures. In order to understand the driving mechanism in these oxide systems, tantalum oxide films are resistively switched by conductive atomic force microscopy (C‐AFM), and subsequently imaged by kelvin probe force microscopy (KPFM) and spatially resolved time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). These workflows enable induction and analysis of the resistive switching mechanism as well as control over the resistively switched region of the film. In this work it is shown that the resistive switching mechanism is driven by both current and electric field effects. Reversible oxygen motion is enabled by applying low (<1 V) electric fields, while high electric fields generate irreversible breakdown of the material (>1 V). Fully understanding oxygen motion and electrical effects in bilayer oxide memristor systems is a fundamental step toward the adoption of memristors as a neuromorphic computing technology.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks.

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.

Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing.

This review article attempts to provide a comprehensive review of the recent progress in cerium oxide (CeO2)-based resistive random-access memories (RRAMs). CeO2 is considered the most promising candidate because of its multiple oxidation states (Ce3+ and Ce4+), remarkable resistive-switching (RS) uniformity in DC mode, gradual resistance transition, cycling endurance, long data-retention period, and utilization of the RS mechanism as a dielectric layer, thereby exhibiting potential for neuromorphic computing. In this context, a detailed study of the filamentary mechanisms and their types is required. Accordingly, extensive studies on unipolar, bipolar, and threshold memristive behaviors are reviewed in this work. Furthermore, electrode-based (both symmetric and asymmetric) engineering is focused for the memristor's structures such as single-layer, bilayer (as an oxygen barrier layer), and doped switching-layer-based memristors have been proved to be unique CeO2-based synaptic devices. Hence, neuromorphic applications comprising spike-based learning processes, potentiation and depression characteristics, potentiation motion and synaptic weight decay process, short-term plasticity, and long-term plasticity are intensively studied. More recently, because learning based on Pavlov's dog experiment has been adopted as an advanced synoptic study, it is one of the primary topics of this review. Finally, CeO2-based memristors are considered promising compared to previously reported memristors for advanced synaptic study in the future, particularly by utilizing high-dielectric-constant oxide memristors.

Mg dopant induced ultra-high HRS resistance and striking switching window characteristics in amorphous Y2O3 film-based memristors

Y2O3 has attracted attention as the representative emerging candidate of a resistive switching (RS) medium in memristors due to its excellent electrical properties and good thermal stability. However, many challenges for Y2O3 film-based memristors remain to be resolved, particularly for the small switching window. Here, the doping engineering strategy is proposed, and in particular, the Mg doped amorphous Y2O3 film is adopted as the RS layer to construct memristors. The prepared Pt/Mg:Y2O3/Pt memristor exhibits a typical reproducible bipolar switching behavior with ultra-high HRS resistance and excellent switching window (>105), compared with the undoped counterparts (∼50). In addition, the multilevel storage capability is also achieved by controlling compliance current. Furthermore, the mechanisms and corresponding physical models for the striking RS characteristics for Pt/Mg:Y2O3/Pt memristors, stemming from the Mg dopant, are discussed and illustrated in detail. This work affords a deep understanding of RS mechanisms for Mg-doped Y2O3 film-based memristors and provides an effective strategy to enlarge the switching window for other transition metal oxide memristors.

Ultrathin SrTiO3-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing

Abstract Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here, we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO3/Nb: SrTiO3 heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity (STP/LTP), transition from STP to LTP, learning–forgetting–relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give rise to the coexistence of volatile- and nonvolatile-type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.

Copper oxide memristor as artificial synapses emulating Hebbian symmetric and asymmetric learning behavior for neuromorphic computing beyond von Neumann architecture

Beyond von Neumann’s architecture, artificial neural network-based neuromorphic computing in a simple two-terminal resistive switching device is considered the future potential technology for simultaneous data processing and storage. These are also compatible with low-power consumption nanoelectronic devices and, thus, suitable for applications such as image recognition toward solving complex pattern recognition problems. Herein, motivated by the human biological brain, we successfully synthesized low-cost RRAM devices using the thermal oxidation of Cu, i.e., CuO as the active material together with Cu as the top electrode and FTO as the bottom contact for a two-terminal resistive switching device, and investigated characteristics for neuromorphic computing. Cu/CuO/FTO-based devices showed excellent bipolar analog RRAM characteristics with 150 repeatable cycles, retention for 11 000 s, and DC pulse endurance for 5000 cycles. Moreover, devices exhibit a remarkable mimicking ability, demonstrating spike time-dependent plasticity (STDP), pulse-paired facilitation (PPF), synaptic weight, and learning and forgetting characteristics, substantiating the recognition ability. Furthermore, the artificial neural network synaptic membrane exhibits excellent long-term (LTP) and short-term (STP) potentiation for six consecutive cycles. Thus, the present work on Cu/CuO/FTO-based devices provides a detailed understanding of CuO active material-based resistive switching with a potential for neuromorphic computing beyond the von Neumann architecture.

EELS Characterization of Niobium Oxide Memristor Devices.

EELS Characterization of Niobium Oxide Memristor Devices.

Operando STEM and EELS Study of Oxide Memristor Devices.

Operando STEM and EELS Study of Oxide Memristor Devices.

Resistive Switching: Physics, Devices and Applications

Resistive Switching: Physics, Devices and Applications

A robust graphene oxide memristor enabled by organic pyridinium intercalation for artificial biosynapse application

A robust graphene oxide memristor enabled by organic pyridinium intercalation for artificial biosynapse application