Oxide Memristors Research Articles

Aliovalent Ta-Doping-Engineered Oxygen Vacancy Configurations for Ultralow-Voltage Resistive Memory Devices: A DFT-Supported Experimental Study.

Alteration of transport properties of any material, especially metal oxides, by doping suitable impurities is not straightforward as it may introduce multiple defects like oxygen vacancies (Vo) in the system. It plays a decisive role in controlling the resistive switching (RS) performance of metal oxide-based memory devices. Therefore, a judicious choice of dopants and their atomic concentrations is crucial for achieving an optimum Vo configuration. Here, we show that the rational designing of RS memory devices with cationic dopants (Ta), in particular, Au/Ti1-xTaxO2-δ/Pt devices, is promising for the upcoming non-volatile memory technology. Indeed, a current window of ∼104 is realized at an ultralow voltage as low as 0.25 V with significant retention (∼104 s) and endurance (∼105 cycles) of the device by considering 1.11 at % Ta doping. The obtained device parameters are compared with those in the available literature to establish its excellent performance. Furthermore, using detailed experimental analyses and density functional theory (DFT)-based first-principles calculations, we comprehend that the meticulous presence of Vo configurations and the columnar-like dendritic structures is crucial for achieving ultralow-voltage bipolar RS characteristics. In fact, the dopant-mediated Vo interactions are found to be responsible for the enhancement in local current conduction, as evidenced from the DFT-simulated electron localization function plots, and these, in turn, augment the device performance. Overall, the present study on cationic-dopant-controlled defect engineering could pave a neoteric direction for future energy-efficient oxide memristors.

Tactile Near-Sensor Analogue Computing for Ultrafast Responsive Artificial Skin.

Ultrafast artificial skin enables unprecedented tactile internet applications in prosthetics, robotics, and human-machine interactions. However, current artificial skin systems that rely on front-end interface electronics typically perform redundant data transfer and analogue-to-digital conversions for decision-making, causing long latency (milliseconds). Here, a near-sensor analogue computing system based on a flexible memristor array for artificial skin applications is reported. This system, which seamlessly integrates a tactile sensor array with a flexible hafnium oxide memristor array, can simultaneously sense and compute raw multiple analogue pressure signals without interface electronics. As a proof-of-concept, the system is used for real-time noise reduction and edge detection of tactile stimuli. One sensing-computing operation of this system takes about 400ns and consumes on average 1000 times less power than a conventional interface electronic system. The results demonstrate that near-sensor analogue computing offers an ultrafast and energy-efficient route to large-scale artificial skin systems.

Performance Optimization of Atomic Layer Deposited HfOx Memristor by Annealing With Back-End-of-Line Compatibility

Hafnium oxide (HfO_<i>x</i>) memristor has attracted enormous attention due to its high performance and back-end-of-line (BEOL) compatibility, thus providing a novel approach to implementing artificial intelligence neural networks. In this work, great performance optimization of HfO_<i>x</i> memristor has been achieved by using atomic layer deposition (ALD) method and post-metal annealing (PMA) process, in which both procedures are with low-temperature budget (&#x003C; 300 &#x00B0;C) and are compatible with CMOS BEOL process. The device exhibits forming-free, high yield, good linearity, fast speed and non-volatile characteristics. Besides, the device conductance can be well modulated by using the most desired pulse protocol, namely the identical pulse with same pulse amplitude and width. More than 3bit stable conductance states have been obtained, indicating its great potential in practical memristor neuromorphic computing system.

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.

Tunable Switching Behavior of GO-Based Memristors Using Thermal Reduction.

This work reports on the fabrication of a novel planar reduced graphene oxide (rGO) memristor (MR) device. For the first time in the literature, the MR tunable resistive switching behavior is controlled by the GO reduction time at a constant temperature. The device is fabricated using standard microfabrication techniques on a flexible cyclic olefin copolymer substrate (COC). Thermal reduction of the GO layer at low temperatures (100 °C) avoids the drawbacks of chemical reduction methods such as toxicity and electrode metal damage during fabrication, while allowing for fine-tuning of the MR’s switching behavior. The device has analog switching characteristics, with a range of different resistance states. By taking advantage of the slow nature of GO thermal annealing, the switching properties of the rGO memristors can be precisely controlled by adjusting the reduction period. At short annealing times (i.e., T < 20 h), the devices switch from high to low resistance states, while at longer annealing times the switching behavior is reversed, with the device switching from low to high resistance states (LRS to HRS). Resistive switching occurs as a result of the diffusion and removal of the oxygen functional groups in the GO film caused by Joule heating induced by the electric current. Complete electrical characterization tests are presented along with wettability and X-ray diffraction (XRD) tests. This work opens a new vision for realizing rGO-based MR devices with tunable switching properties, broadening the application horizon of the device.

Impact of titanium doping and pulsing conditions on the analog temporal response of hafnium oxide based memristor synapses

Hafnium oxide non-volatile memories have shown promise as an artificial synapse in neuromorphic computing architectures. However, there is still a need to fundamentally understand how to reliably control the analog resistance change induced by oxygen ions that partially rupture or re-form the conductive filament. In this work, the impact of measurement conditions (pulse amplitude and pulse width) and titanium dopants on the analog resistance change of atomic layer deposited hafnium oxide memristor synapses are studied. A lower pulse amplitude improves the linearity of resistance change as a function of the number of pulses but results in a smaller memory window. The addition of titanium dopants does not substantively change the analog resistance modulation of hafnium oxide. Density functional theory calculations show that titanium strongly impacts oxygen ion motion in the HfxTiyOz matrix but does not impact significantly in the HfTi metallic filament. This study demonstrates that the analog characteristic of HfxTiyOz artificial synapses is largely independent of the titanium doped bulk oxide since the resistance change is primarily controlled by the HfTi metallic conducting filament.

Electrode-dependent electrical switching characteristics of InGaZnO memristor

To improve the electrical characteristics of an analog indium–gallium–zinc oxide (IGZO) memristor, two kinds of IGZO memristive device with different electrode materials were fabricated, and their electrical characteristics and switching mechanisms were analyzed. It was confirmed that the endurance and retention characteristics of the IGZO memristive device were improved, which was explained by the analysis of operation mechanism. Analog IGZO memristive devices operate through interfacial switching based on the Schottky barrier modulation resulting from the generation and recombination of oxygen vacancies, but the devices with silicon electrode perform analog switching based on conductive filaments through the forming process. The retention time of both devices was measured and estimated with exponential decay functions, and the pulse-induced learning characteristics were also verified. The electrical and synaptic characteristics were considered in the modified national institute of standards and technology (MNIST) database pattern recognition test, and the effects of retention and linearity of weight-update on the recognition rate were analyzed.

Memristor compact model with oxygen vacancy concentrations as state variables

We present a unique compact model for oxide memristors based upon the concentration of oxygen vacancies as state variables. In this model, the increase (decrease) in oxygen vacancy concentration is similar in effect to the reduction (expansion) of the tunnel gap used as a state variable in existing compact models, providing a mechanism for the electronic current to increase (decrease) based upon the polarity of the applied voltage. Rate equations defining the dynamics of state variables are obtained from simplifications of a recent paper in which electronic processes (i.e., electron capture/emission) were combined with atomic processes (i.e., Frenkel-pair generation/recombination, diffusion) stemming from the thermochemical model of dielectric breakdown. Central to the proposed model is the effect of the electron occupancy of oxygen vacancy traps on resistive switching dynamics. The electronic current is calculated considering Ohmic, band-to-band, and bound-to-band contributions. The model includes uniform self-heating with Joule heating and conductive loss terms. The model is calibrated using experimental current–voltage characteristics for HfO2 memristors with different electrode materials. Though a general model is presented, a delta-shaped density of states profile for oxygen vacancies is found capable of accurately representing experimental data while providing a minimal description of bound-to-band transitions. The model is implemented in Verilog-A and tested using read/write operations in a 4×4 1T1R nonvolatile memory array to evaluate its ability to perform circuit simulations of practical interest. A particular benefit is that the model does not make strong assumptions regarding filament geometry of which scant experimental-evidence exists to support.

A Compact and Continuous Reformulation of the Strachan TaOx Memristor Model With Improved Numerical Stability

We present a compact, continuous, and numerically stable version of a tantalum oxide (TaOx) memristor model which can be employed for robust and reliable simulations of large scale memristor based circuits. The original model contains a piecewise differentiable function in the memductance expression and discontinuous step functions in the state equation. Additionally, the original model does not set a proper upper bound for the state variable and may admit blowing up solutions due to an exponential power term, preventing the use of it for numerically reliable simulations. Considering these drawbacks, we modify the original model so as to i) simplify the memductance function while removing its piecewise differentiable nonlinearity, ii) include a proper window function for the ON state dynamics, which is missing in the original model, iii) modify and bound the exponential power term to prevent an uncontrollable blow-up of the solutions, and iv) apply a process called unification, allowing us to remove the step functions inherent in the model, which is a novelty in state-limited memristor models. We validate the accuracy of the proposed model via DC and transient simulations, dynamic route map analysis and a Spice implementation of an anti-series configuration, showing the applicability of the model.

Edge of Chaos Theory Resolves Smale Paradox

No isolated system may ever support complexity. Emergent phenomena may however appear in an open system, if, as established by the Edge of Chaos theory, some of its constitutive elements feature the capability to amplify infinitesimal fluctuations in energy, provided an external source supplies them with a sufficient amount of DC power, which is known to be a signature for locally-active behaviour. In particular, complex behaviours, including static and dynamic pattern formation, may emerge in arrays of identical diffusively-coupled cells, if and only if the basic unit is poised on a particular sub-domain of the Local Activity regime, referred to as Edge of Chaos, within which a quiet state hides in fact a high degree of excitability. Here we show, for the first time, that these counterintuitive phenomena may emerge in a basic memristor cellular neural network, consisting of two identical diffusively-coupled second-order cells. The proposed bio-inspired array represents the simplest ever-reported open system, which reproduces the shocking phenomenon, reported by Smale in 1974, when, while studying a model from cellular biology, he observed two identical reaction cells, “mathematically dead” on their own, pulsating together upon diffusive coupling. Impressively, the bio-inspired two-cell reaction-diffusion network contains only nine circuit elements, specifically two DC voltage sources, three linear resistors, two linear capacitors, and two functional niobium oxide (NbO) memristors from NaMLab. Applying the theory of Local Activity to an accurate model of the memristor oscillator, a comprehensive picture for its local and global dynamics may be drawn, providing a systematic method to tune the design parameters of the two-cell array to enable diffusion-driven instabilities therein.

Highly transparent flexible artificial nociceptor based on forming-free ITO memristor

Designing a flexible transparent electronic device with biological functions is of great interest for the future wearable integrated artificial intelligence equipment. Nociceptor is a vitally important receptor of sensory neuron, which is responsible for providing a warning signal by recognizing noxious stimuli to reduce potential physical injury. Here, a flexible transparent artificial nociceptor device is demonstrated to simulate the biological nociceptor functions based on the indium tin oxide (ITO) memristor, which exhibits forming-free and reproducible threshold resistive switching behaviors. This structurally simple memristor can imitate the key features of biological nociceptor, including “threshold,” “relaxation,” and “no adaptation” behaviors and sensitization phenomena of hyperalgesia and allodynia upon external stimuli. Finally, an alarm system is built to demonstrate the simplicity and feasibility of this artificial nociceptor for future neuromorphic systems. These results indicate a potential application of the ITO memristor in the future flexible invisible neuromorphic cognitive platform.

Current–voltage characteristics of oxide memristor from single filament configuration

Current–voltage characteristics of oxide memristor from single filament configuration

Electronic synaptic characteristics and simulation application of Ag/CeO2/Pt memristor

With the development of science and technology, artificial neural networks (ANNs) have become a research spot. Furthermore, two-terminal oxide memristors with adjustable resistance have attracted extensive attention due to their simple structure, low power consumption, and easy integration, among other attractive features. Additionally, among many oxides, ceria has exhibited good performance, such as longer retention and better stability in resistive devices; however, it was currently rarely used in artificial neural synapses. In this work, a self-designed Ag/CeO2/Pt memristor was found to realize the slow transition between the high-resistance state (HRS) and low-resistance state (LRS) at a very small working voltage. It was also found to exhibit very good retention performance and cyclic characteristics. The conductivity of the device was analyzed by the current-voltage (I–V) characteristics curve. Furthermore, its artificial synaptic function was explored, and a series of neuromorphic systems simulations were carried out. Additionally, the relationships between the pulse sequence parameters and the resistance state of the device were explored, and an electrical signal simulation of Pavlov's dog experiment was designed. The findings demonstrated that the device could be used to realize the application of artificial neural synapse simulation.

A Simplified Tantalum Oxide Memristor Model, Parameters Estimation and Application in Memory Crossbars

In this paper, an improved and simplified modification of a tantalum oxide memristor model is presented. The proposed model is applied and analyzed in hybrid and passive memory crossbars in LTSPICE environment and is based on the standard Ta2O5 memristor model proposed by Hewlett–Packard. The discussed modified model has several main enhancements—inclusion of a simplified window function, improvement of its effectiveness by the use of a simple expression for the i–v relationship, and replacement of the classical Heaviside step function with a differentiable and flat step-like function. The optimal values of coefficients of the tantalum oxide memristor model are derived by comparison of experimental current–voltage relationships and by using a procedure for parameter estimation. A simplified LTSPICE library model, correspondent to the analyzed tantalum oxide memristor, is created in accordance with the considered mathematical model. The improved and altered Ta2O5 memristor model is tested and simulated in hybrid and passive memory crossbars for a state near to a hard-switching operation. After a comparison of several of the best existing memristor models, the main pros of the proposed memristor model are highlighted—its improved implementation, better operating rate, and good switching properties.

A Generalized Workflow for Creating Machine Learning-Powered Compact Models for Multi-State Devices

The predictive capability of existing physical descriptions of multi-state devices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) - based approach to develop a compact model that retains the full physical description of these devices. ML-based modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to - (i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R2 of 0.977371 on our testing data.

Understanding composite negative differential resistance in niobium oxide memristors

Volatile memristors, or threshold switching devices, exhibit a diverse range of negative differential resistance (NDR) characteristics under current-controlled operation and understanding the origin of these responses is of great importance for exploring their potential as nano-scale oscillators for neuromorphic computing. Here we use a previously developed two-zone, parallel memristor model to undertake a systematic analysis of NDR modes in two-terminal metal-oxide-metal devices. The model assumes that the non-uniform current distribution associated with filamentary conduction can be represented by a high current density core and a lower current-density shell where the core is assumed to have a memristive response due to Poole-Frenkel conduction and the shell is represented by either a fixed resistor or a second memristive region. A detailed analysis of the electrical circuits is undertaken using a lumped-element thermal model of the core-shell structure, and is shown to reproduce continuous and discontinuous NDR responses, as well as more complex compound behaviour. Finally, an interesting double-window oscillation behaviour is predicted and experimentally verified for a device with compound NDR behaviour. These results clearly identify the origin of different NDR responses and provide a strong basis for designing devices with complex NDR characteristics.

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO2 has been accepted as one of the most promising resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO2-based memristors. Here, the conductive filament system in the amorphous HfO2-based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf6O and its crystalline surroundings (monoclinic or tetragonal HfOx). The phase of the HfOx shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO2 is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO2-based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications.

Role of an oxide interface in a resistive switch

In the present era of data-driven architectures like 5G, Internet of things (IoT), Artificial Intelligence (AI), etc, the requirement of fast-switchable memory storage is more than ever. Oxide resistive switches are considered to be a primary choice in the non-volatile memory design. In this work, we have engineered the conventional metal-insulator-metal (MIM) structure of an oxide memristor (Ag/ZnO/ITO) by inducing an additional oxide layer La0.7Sr0.3MnO3 (LSMO) at the interface between the active layer (ZnO) and Ag electrode. The presence of LSMO acts as a reservoir for the oxygen vacancies, easing the conducting filament formation process in ZnO, thereby enabling drastic improvement of the switching performance and offering reliable endurance over multiple switching cycles. First-principles-based calculations suggested the role of oxygen vacancies in controlling the electronic state of ZnO and formation of vacancies in the resistive switching process, which is in agreement with the experimental observation. The current results pave ways for improving the switching performance of resistive memory circuits through simple structural engineering incorporation, which lies at the heart of oxide electronics.

Multilevel switching in Mg-doped HfOx memristor through the mutual-ion effect

HfOx memristor is one of the most promising candidates for nonvolatile memory and neuromorphic computing applications, but for the latter, its gradual conduction modulation and switching linearity still need improvement. In this work, uniform and multilevel resistance switching (RS) of HfOx memristors have been achieved through Mg doping, which is a distinct dopant other than previously used elements like Al and Gd. Moreover, the Mg-doped HfOx memristor exhibits excellent continuous conductance modulation characteristics under pulse voltage. First-principles calculations and conductive mechanism analysis show that Mg can migrate into the conductive filaments (CFs) by adjusting oxygen vacancy migration and defect states in the CFs, resulting in the improved RS performance of HfOx memristors. The reason why Mg doping rather than Ca doping could yield improved RS performances is clarified. Our work shows the unique functionality of Mg dopants in HfOx and provides a feasible way to improve RS performance of transition metal oxide memristors and its guiding principles.

Bias Polarity Dependent Threshold Switching and Bipolar Resistive Switching of TiN/TaOx/ITO Device

In this work, we demonstrate the threshold switching and bipolar resistive switching with non-volatile property of TiN/TaOx/indium tin oxide (ITO) memristor device. The intrinsic switching of TaOx is preferred when a positive bias is applied to the TiN electrode in which the threshold switching with volatile property is observed. On the other hand, indium diffusion could cause resistive switching by formation and rupture of metallic conducting filament when a positive bias and a negative bias are applied to the ITO electrode for set and reset processes. The bipolar resistive switching occurs both with the compliance current and without the compliance current. The conduction mechanism of low-resistance state (LRS) and high-resistance state (HRS) are dominated by Ohmic conduction and Schottky emission, respectively. Finally, threshold switching and bipolar resistive switching are verified by pulse operation.