Oxide Resistive Random Access Memory Research Articles

A cold-electrode metal–oxide resistive random access memory

To reduce the leakage and power consumption of metal–oxide resistive random access memory (RRAM), we propose and fabricate a cold-electrode (CE) RRAM (CE-RRAM) by extending the mechanism of cold-source FETs. First-principles calculations show that the n-Si/TiN composite CE can filter electrons with energy within the Si bandgap, which contribute to leakage current. A n-Si/TiN/HfOx/Pt CE-RRAM with low leakage current and large on/off current ratio was designed and fabricated. Comparative analysis with conventional RRAM demonstrates over a 100-fold reduction in leakage current in a high resistance state and a tenfold improvement in the Ion/Ioff ratio. Additionally, the CE-RRAM effectively suppresses the overshoot effect in terminal I–V characteristics and exhibits good endurance, maintaining a 100 Ion/Ioff ratio after 104 cycles. Furthermore, even after 104 s at 100 °C, the state remains unchanged. Moreover, the CE-RRAM demonstrates its multi-level storage capability.

A Study on Unified Modelling Approach for Memristor: Next Generation Semiconductor Devices

This research investigates the voltage-current (V-I) characteristics of three distinct memristor models, each representing a unique composition and behaviour. The model, denoted as TiN-TiOx-HfOx-Pt-Bilayered Memristor model, is a compact representation designed for Metal–Oxide Resistive Random Access Memory (RRAM). Matlab tool is used for the simulation to analyse V-I characteristics of Memristor. Through comprehensive analysis and simulations, we aim to provide a detailed insight into the intrinsic behaviours of the memristor models. Understanding the V-I characteristics of these models is crucial for their potential applications in emerging memory technologies. This Study elaborate the mathematical modelling of memristor with the outcome untuned V-I Characteristics - DC Switching characteristics. The findings contribute to the broader field of memristor research, fostering advancements in electronic memory devices and computational systems

Ultra-high ON/OFF ratio with low set voltage for a Pt-Nb2O5-Pt resistivity switching device

The limited ON/OFF ratio constrains the quantity of available resistance states and the multi-level storage capability, a high ON/OFF ratio (>103) is essential for implementing multi-level storage in a resistive device. Through annealing processes for Nb2O5 films prepared by magnetron sputtering, we successfully achieved an ultra-high ON/OFF ratio of 106 and a four-order higher resistance compared to conventional binary oxide resistive random-access memory. Results confirm that annealing can effectively modulate the content of oxygen vacancies within the resistance-switching layer and the formation path of oxygen vacancies. At the same time, we observed a gradual decrease in the set voltage of the device with increasing annealing temperature, which is beneficial for the low power consumption characteristics. Therefore, appropriate annealing is a novel and effective approach to optimize device performances for better information storage applications, and its core mechanism is to modulate the concentration of Vo for constructing fixed conductive paths.

Optimization of Bilayer Resistive Random Access Memory Based on Ti/HfO2/ZrO2/Pt.

In this paper, the electrothermal coupling model of metal oxide resistive random access memory (RRAM) is analyzed by using a 2D axisymmetrical structure in COMSOL Multiphysics simulation software. The RRAM structure is a Ti/HfO2/ZrO2/Pt bilayer structure, and the SET and RESET processes of Ti/HfO2/ZrO2/Pt are verified and analyzed. It is found that the width and thickness of CF1 (the conductive filament of the HfO2 layer), CF2 (the conductive filament of the ZrO2 layer), and resistive dielectric layers affect the electrical performance of the device. Under the condition of the width ratio of conductive filament to transition layer (6:14) and the thickness ratio of HfO2 to ZrO2 (7.5:7.5), Ti/HfO2/ZrO2/Pt has stable high and low resistance states. On this basis, the comparison of three commonly used RRAM metal top electrode materials (Ti, Pt, and Al) shows that the resistance switching ratio of the Ti electrode is the highest at about 11.67. Finally, combining the optimal conductive filament size and the optimal top electrode material, the I-V hysteresis loop was obtained, and the switching ratio Roff/Ron = 10.46 was calculated. Therefore, in this paper, a perfect RRAM model is established, the resistance mechanism is explained and analyzed, and the optimal geometrical size and electrode material for the hysteresis characteristics of the Ti/HfO2/ZrO2/Pt structure are found.

Stability Enhancement in Copper-Doped Iron Oxide Resistive Random Access Memory via RF Co-Sputtering

In the study, the ITO/Cu-doped Fe2O3/ITO thin film RRAM is prepared using an RF sputtering system. The XRD pattern shows that the Cu:Fe2O3 thin film has a rhombohedral structure and does not display secondary or impurity phases for copper. Results revealed that the standard deviation and average voltage of Cu:Fe2O3 thin film are −1.98 and 0.92 V for Vset, respectively, while those for Vreset are 1.31 and 0.39 V, respectively. The resistive switching cycles and data retention test times of the Cu:Fe2O3 thin film device show that the on/off ratio is 39.4 and over 104 s. These results indicated that the Cu-doped Fe2O3 thin film can improve the performance of RRAM.

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.

Physical model simulations of Hf oxide resistive random access memory device with a spike electrode structure

Resistive memory has become an attractive new memory type due to its outstanding performance. Oxide-based resistive random access memory is one type of widely used memory whose resistance can be transformed by applying current or voltage. Memristors are widely used in various kinds of memories and neural morphological calculations. Therefore, it is of vital importance to understand the physical change mechanism of an internal memristor under stimulation to improve electrical properties of the memristor. In our studies, a device model based on Hf oxide was proposed, then completely processes of the forming, reset and set were simulated. Meantime, the generation and recombination of oxygen vacancies were considered in all the processes, making the simulation more practical. In addition, a spike electrode structure was applied, a gathering electric field can be generated in the oxide layer so that the improved device has a faster forming voltage, lower forming current and lower instantaneous power consumption in the ON state. Finally, the effects of spike electrode length on the forming process were studied, the research results reveal that a longer probe electrode can engage a lower forming voltage and accelerate the formation of conductive filaments.

Spatially-Resolved Thermometry of Filamentary Nanoscale Hot Spots in TiO2 Resistive Random Access Memories to Address Device Variability.

Resistive random access memories (RRAM), based on the formation and rupture of conductive nanoscale filaments, have attracted increased attention for application in neuromorphic and in-memory computing. However, this technology is, in part, limited by its variability, which originates from the stochastic formation and extreme heating of its nanoscale filaments. In this study, we used scanning thermal microscopy (SThM) to assess the effect of filament-induced heat spreading on the surface of metal oxide RRAMs with different device designs. We evaluate the variability of TiO2 RRAM devices with area sizes of 2 × 2 and 5 × 5 μm2. Electrical characterization shows that the variability indicated by the standard deviation of the forming voltage is ∼2 times larger for 5 × 5 μm2 devices than for the 2 × 2 μm2 ones. Further knowledge on the reason for this variability is gained through the SThM thermal maps. These maps show that for 2 × 2 μm2 devices the formation of one filament, i.e., hot spot at the device surface, happens reliably at the same location, while the filament location varies for the 5 × 5 μm2 devices. The thermal information, combined with the electrical, interfacial, and geometric characteristics of the device, provides additional insights into the operation and variability of RRAMs. This work suggests thermal engineering and characterization routes to optimize the efficiency and reliability of these devices.

Sol-Gel-Processed Y2O3 Multilevel Resistive Random-Access Memory Cells for Neural Networks.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol-gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y2O3 leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y2O3 RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y2O3 RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.

Nitrogen-Oxyanion-Doped HfO2 Resistive Random-Access Memory With Chemically Enhanced Forming

The high forming voltage of resistive random-access memory (RRAM) is one of the key bottlenecks for its integration in advanced silicon technology nodes. Here a nitrogen-oxyanion-doped (N-doped) hafnium oxide (HfO2) RRAM with overall improvement on forming voltage, on/off ratio and endurance is demonstrated. The critical electric field of N-doped RRAM for forming is 40% less than that of undoped RRAM. Therefore, a thicker resistive switching layer (RSL) can be used to obtain the same forming voltage, which benefits the on/off ratio and endurance performance. The N-doped RRAM achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> improvement in on/off ratio and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> improvement in endurance at the forming voltage of 2 V, a value applicable for integration with advanced silicon technology node. We propose the hypothesis that the nitrites ((NO2)−) in the RSL of N-doped devices promote forming through introducing extra chemical process as well as additional conductive paths. Physical characterization and first-principles calculations are further carried out to validate our hypothesis.

Formation of Remote Quantum Point Contact Inside Dielectric Medium of an AlO x /SiO x ReRAM

Remote quantum point contact (QPC) far from the metal electrodes was observed in forming free aluminum oxide (AlOx)/silicon oxide (SiOx) resistive random access memory (ReRAM) cell, which was resolved at low bias. QPC behavior of the cell was attributed to confined oxygen vacancies across uncorrelated narrow grain boundaries of intermediate O3 treated thin AlOx layer of the stack, which was designed to restrict vacancy channeling between the two immediate reservoirs; i.e., from beneath SiOx to the top as-deposited AlOx layer. From time dependent dielectric breakdown (TDDB) characteristics of the cell while assessed under low stress biases, we found one to one correspondence between TDDB and its bias dependent QPC. Furthermore, we found that, under steady state and especially at low biases progressive breakdown (PBD) of the dielectric exhibited two-level random telegraph noise (RTN) like conductance fluctuations, which occurred in the magnitude of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${G}_{{0}}$ </tex-math></inline-formula> or in its multiples depending on applied bias to the cell, that demonstrated a correlation between electron trapping and maximum QPC instability during filament growth. Low voltage QPC-based switching under dc bias sweep, its correlation with TDDB and RTN under the steady state thus provided us a framework to analyze the evolution of QPC well before reaching the SET state in an AlOx/SiOx cell, which is different from the usual QPC evolution as found near contact regions in a conventional SiOx cell without accompanying engineered AlOx barrier.

Study on ion dynamics of hafnium oxide RRAM by electrode thermal effect

Resistive random access memory (RRAM) has become one of the representatives of the next generation of nonvolatile memory because of its excellent performance, and the data reading function is caused by the growth and fracture of the conductive filament (CF) in the switch layer. Therefore, the study of the thermal properties of materials on the conductive mechanism is a step that cannot be ignored. In this paper, a numerical model of filament dissolution caused by the coupling effect of Joule heat and electric field in resistance elements is established. The influence of the thermal field on the electric field and ion mobility during device setup and reset is explained by solving partial differential equations. On this basis, the importance of electrode parameters for resistive switching behavior is emphasized. The results show that the best performance can be obtained at 298 K when TiN is selected as the top electrode material of the equipment. This work has deepened the understanding of RRAM resistance switch characteristics and has guiding significance for selecting memristor materials and promoting its industrialization process.

The influence of device structure on resistance switching in PbS QDs film inserted RRAM

The introduction of PbS QD (quantum dot) films has been proved, dramatically, to optimize the resistive switching (RS) performance in oxide resistive random access memory. In order to optimize parameters to a greater extent, the necessity of in-depth understanding of the resistance switching mechanism is self-evident. In this paper, PbS QD layers were inserted into a Ta/AlOxNy/Pt structure device in different positions in order to investigate the influence of the device structure in the PbS QD film inserted device. The Ta/PbS QDs/AlOxNy/Pt device with a Ta anion reservoir and a PbS QD film anion reservoir in the same direction exhibits excellent optimization of parameters, which is ideal for low-power devices. A model is constructed to elaborate the resistive switching process. Moreover, modulation of PbS QD film thickness on RS has been studied. A device with middle thickness of the PbS QD films combines low voltage, low current, and excellent stability, which is believed to be a favorable structure for the PbS QD inserted device.

Flexible Transparent High‐Efficiency Photoelectric Perovskite Resistive Switching Memory

AbstractPerovskite resistive random‐access memory (RRAM) is a promising candidate for next‐generation logic, adaptive and nonvolatile memory devices, because of its high ON/OFF ratio, low‐cost fabrication, and good photoelectric regulation performance. In this work, a flexible transparent CsPbBr3 quantum dots (QDs) mixed in graphene oxide (GO) RRAM device is introduced, which is controllable by both an electric field and illumination. Under illumination, the ON/OFF ratio of the Ag/CsPbBr3 QDs:GO/ITO device is ≈1.4 × 107, which is 1077 times larger than that in the dark condition (1.3 × 104). The SET/RESET voltages are +2.28/−2.04 V and +1.68/−1.08 V under the dark and illumination conditions, respectively. As a flexible memory device, the resistances are little affected by the bending curvatures and load‐cycling. Before and after 104 bending cycles with a radius of 5 mm under illumination, the ON/OFF ratios keep in the same order, which are 2.5 × 107 and 2.3 × 107, respectively. The ratio values are 8.8 × 104 and 2.9 × 104 under the dark condition, respectively. This innovative resistive memory based on the CsPbBr3 QDs:GO hybrid film supports a huge space for the development of photoelectrical dual‐controlled flexible RRAM devices.

Forming-free bipolar resistive switching characteristics in Al/Mn3O4/FTO RRAM device

The bipolar resistive switching (BRS) phenomenon was demonstrated in Mn3O4 using an aluminium/Mn3O4/fluorine-doped tin oxide resistive random access memory (RRAM) device. The fabricated RRAM device showed good retention, non-volatile behaviour, and forming-free BRS. The current–voltage characteristics and the temperature dependence of the resistance measurements were used to explore conduction mechanisms, thermal activation energy, and temperature coefficient of resistance. The resistance ratio of the high resistance state (HRS) to the low resistance state (LRS) was ∼102. The device showed different conduction mechanisms in LRS and HRS modes, such as ohmic conduction and space charge limited conduction. The formation and rupture of conducting filaments of oxygen vacancies took place by changing the polarity of external voltage, which may be responsible for resistive switching characteristics in the device. This device is suitable for application in future high-density non-volatile memory RRAM devices.

Effects of Ambient and Annealing Temperature in HfO2 Based RRAM Device Modeling and Circuit-Level Implementation

This article focuses on the relevance of the effect of ambient temperature and annealing in the context of compact modeling of metal oxide resistive random access memory (RRAM) devices. The ambient temperature affects the conduction characteristic of resistive switching memories, so it becomes an essential factor to include when adjusting the experimental data. Reported the fabricated results and memory switching parameters with the defined set (Vset) and reset (Vreset) transition voltages for the fabricated annealed HfO2-based RRAM. Additionally, to illustrate the importance of this characteristic in the form of the I-V curve, the Stanford model (SFM) for RRAM devices is enhanced by incorporating the annealing temperature as an additional parameter in the script of the Verilog-A model. Stanford and modified Stanford model (MSFM) are analyzed at the device level using cadence circuit simulator and implemented in the nonvolatile memory circuit (3 *3 memory arrays). Results confirmed that the experimental switching voltages, Vset, Vreset are 1.7 V, −0.8 V. These values are well suited along the simulated MSFM switching voltages of, Vset, Vreset (1.8 V, −0.7 V). The mean error percentage of the MSF is 18.42%.

Fabrication of Zinc Oxide Resistive Random-Access Memory on a Flexible Polyimide Substrate with Different Thicknesses

In this study, a resistive random-access memory (RRAM) with a metal-insulator-metal structure was fabricated on polyimide substrates with different thicknesses, and the insulating layer was deposited with zinc oxide material by the sol-gel method. The electrical properties of the RRAM devices were evaluated using bending tests. In the bending tests, the operating electric field increased and the retention time decreased; nevertheless, the electrical properties of the device with a thicker polyimide substrate were less affected. The results of this study will help improve the characteristics of RRAMs on soft boards.

Physics-Based Stochastic Three-Dimensional Modeling for Metal–Oxide Resistive Random Access Memory

In this work, a fully physics-based stochastic 3-D modeling of metal–oxide-based resistive random access memory (RRAM) device has been conducted. In contrast to general 1-D or 2-D modeling of RRAM devices, 3-D modeling is able to provide a picture of the evolution of the 3-D random distributions of oxygen vacancies and temperature inside the device during the forming and SET/RESET processes. The modeling confirms that a tunneling gap between the top electrode (TE) which serves as an oxygen ion reservoir and the tip of the conductive filament may form at a high resistive state, which is assumed in the frequently used compact models of RRAM devices. The modeling shows that the sites with high temperature are concentrated in a cylindrical region at the center of the RRAM device, running from the inert bottom electrode to the TE. The modeling can reproduce both typical current-voltage characteristics of metal–oxide RRAM devices in forming and RESET and SET operations under a dc voltage sweep and the modulation of RRAM conductance by SET/RESET pulses.

3-D Vertical via Nitrogen-Doped Aluminum Oxide Resistive Random-Access Memory

Resistive random-access memory (RRAM) with atomic layer deposited (ALD) nitrogen-doped aluminum oxide (AlO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> ) resistive dielectric appropriate for high-density 3-D vertical via array is demonstrated. With appropriate ALD electrodes, the RRAM exhibits almost forming-free, sub-microamperes programming currents, and appropriate resistance ranges. As the via size is scaled down, the set voltage and reset current decrease and the memory window widens, due to the 3-D nature of the device. Programming endurance of 5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> cycles and retention up to 10 years at 90 °C are demonstrated.

OxRRAM-Based Analog in-Memory Computing for Deep Neural Network Inference: A Conductance Variability Study

Analog in-memory compute (AiMC) is a promising approach to efficiently process deep neural networks (DNNs). Due to its small size and nonvolatility, oxide resistive random access memory (OxRRAM) is being considered as a memory device to map neural network weights in AiMC systems. However, OxRRAM conductance variability can have a severe impact on neural network accuracy. This variability depends on the conductance range, in which the OxRRAM device is programed. In this work, the conductance variability is characterized by a range of operating regimes. Next, the DNN accuracy is determined by applying the measured conductance distributions as noise on the neural network weights during training and inference. The software equivalent accuracy is achieved for only high current compliance values on small data sets. This has implications on the AiMC system, in which this device is intended to be used.