RRAM Devices Research Articles

Theoretical occurrence of quantized chemistry of conduction mechanism in layered perovskite for the application of Resistive Random Access Memory devices

AbstractThe Density Functional Theory (DFT) calculations interpreted the electronic and optical alteration of Ruddlesden–Popper layered perovskite (Sr3Zr2O7) with substitutional doping of Ti‐, Hf‐, and Ti+Hf‐ atoms in place of Zr‐atoms by generating the oxygen vacancies (Vos) defect. Formation energy and phonon calculations confirmed that the studied composites are dynamically stable, and the lattice parameters of the considered RP perovskite with and without vacancy defects did not change by introducing a small concentration of doped elements. The doped Sr3Zr2O7 composites show band gap tuning in the presence and absence of Vos, which was 3.31 eV in pristine form, and localized states near the Fermi line due to dopant and Vos, which confirmed the quantized conductance in all composites and may be beneficial for overcoming uniformity issues in nonvolatile memory devices. Isosurface charge density calculations also verified this result by depicting the physical mechanism of charge accumulation and depletion in the layers of RP perovskite in the vicinity of defects, resulting in residual conducting filaments guiding its growth and leading it to a low resistance state. The photosensitive response of this layered perovskite also confirmed its use for memory storage applications. The valuable outcomes of this study predicted that Sr3Zr2O7+Ti+Hf is the most stable and, hence, the best composite for nonvolatile RRAM device applications.

Improved Resistive and Synaptic Characteristics in Neuromorphic Systems Achieved Using the Double-Forming Process.

In this study, we investigate the electrical properties of ITO/ZrOx/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrOx-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I-V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule.

The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfOx/W Bilayer-Structured Memory Device.

This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments at the interface of ZnO and HfOx contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfOx layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfOx/W structure holds the potential to be a viable memory component for integration into neuromorphic systems.

Coexistence of Electrochromism and Bipolar Nonvolatile Memory in a Single Viologen.

Viologens are fascinating redox-active organic compounds that have been widely explored in electrochromic devices (ECDs). However, the combination of electrochromic and resistive random-access memory in a single viologen remains unexplored. We report the coexistence of bistate electrochromic and single-resistor (1R) memory functions in a novel viologen. A high-performance electrochromic function is achieved by combining viologen (BzV2+2PF6) with polythiophene (P3HT), enabling a "push-pull" electronic effect due to the efficient intermolecular charge transfer in response to an applied bias. The ECDs show high coloration efficiency (ca. 1150 ± 10 cm2 C-1), subsecond switching time, good cycle stability (>103 switching cycles), and low-bias operation (±1.5 V). The ECDs require low power for switching the color states (55 μW cm-2 for magenta and 141 μW cm-2 for blue color). The random-access memory devices (p+2-Si/BzV2+2PF6/Al) exhibit distinct low and high resistive states with an ON/OFF ratio of ∼103, bipolar and nonvolatile characteristics that manifest good performances, and "Write"-"Read"-"Erase" (WRE) functions. The charge conduction mechanism of the RRAM device is elucidated by the Poole-Frenkel model where SET and RESET states arise at a low transition voltage (VT = ±1.7 V). Device statistics and performance parameters for both electrochromic and memory devices are compared with the literature data. Our findings on electrochromism and nonvolatile memory originated in the same viologen could boost the development of multifunctional, smart, wearable, flexible, and low-cost optoelectronic devices.

Graphene-based RRAM devices for neural computing.

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

Influences of Cu Doping on the Microstructure, Optical and Resistance Switching Properties of Zinc OxideThin Films.

Copper-doped zinc oxide films (Zn1-xCuxO) (x = 0, 2%, 4%, 6%) were fabricated on conductive substrates using the sol-gel process. The crystal structure, optical and resistive switching properties of Zn1-xCuxO films are studied and discussed. RRAM is made using Zn1-xCuxO as the resistive layer. The results show that the (002) peak intensity and grain size of Zn1-xCuxOfilms increase from 0 to 6%. In addition, PL spectroscopy shows that the oxygen vacancy defect density of Zn1-xCuxO films also increases with the increase in Cu. The improved resistive switching performance of the RRAM device can be attributed to the formation of conductive filaments and the destruction of more oxygen vacancies in the Zn1-xCuxO film. Consequently, the RRAM device exhibits a higher low resistance state to high resistance state ratio and an HRS state of higher resistance value.

Improved Uniformity of TaOx-Based Resistive Switching Memory Device by Inserting Thin SiO2 Layer for Neuromorphic System.

RRAM devices operating based on the creation of conductive filaments via the migration of oxygen vacancies are widely studied as promising candidates for next-generation memory devices due to their superior memory characteristics. However, the issues of variation in the resistance state and operating voltage remain key issues that must be addressed. In this study, we propose a TaOx/SiO2 bilayer device, where the inserted SiO2 layer localizes the conductive path, improving uniformity during cycle-to-cycle endurance and retention. Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) confirm the device structure and chemical properties. In addition, various electric pulses are used to investigate the neuromorphic system properties of the device, revealing its good potential for future memory device applications.

Impact of Bottom Electrode in HfO2-Based Rram Devices on Switching Characteristics

Resistive random-access memory (RRAM) devices with hydrogen plasma treated HfO2 have shown low power switching (1) and good conductance quantization with programing pulsed operation (2) that qualify them to be used for in-memory computing. Engineering the distribution of defects or oxygen vacancies near the top and bottom electrodes has a significant impact on reducing the switching power and improving the multi-level cell (MLC) characteristics of the device. A graded distribution with higher concentration of oxygen vacancies closer to the top electrode (TE) due to hydrogen plasma treatment and lower concentration near the bottom electrode (BE) reduces switching energy (power) (1). However, the impact of the bottom electrode is not well understood. In this work we investigated three RRAM stacks as shown in Figure 1 that have the same top electrode metal (50nm PVD TiN/5nm ALD TiN) and dielectric (HfO2 with hydrogen plasma treatment at the mid-point) but with different BE for each stack: TiN (device A (control)), TiN plus Mo with thermal anneal (device B), and TiN plus Mo with plasma nitridation (device C). Choosing a more inert metal as the BE provides a lower oxygen vacancy concentration at the interface between the BE and the dielectric (3) which in turn reduces the switching power as noted with device C (TiN plus Mo with plasma nitridation) which switches at a minimum compliance current (Icc) of 500pA. It is possible that the presence of nitrogen prevents the increase in oxygen vacancy concentration near the BE. On the other hand, a higher concentration of oxygen vacancies at the BE and the dielectric interface leads to a larger magnitude of band bending and increases the height of Schottky barrier. This possibly leads to an increase in the switching power in device A and device B as compared to device C (3-4). Note that device A (TiN) switched at Icc=30µA and device B (TiN plus Mo with thermal anneal) switched at Icc=8µA, which is superior compared to device A in switching power. This suggests that the alloy of TiN with Mo is reducing the oxygen vacancies near the BE. To study the MLC characteristics of the devices, we performed a train of 36 RESET pulses with fixed amplitude Vp= -1V and pulse width starting at 4µs and increasing by 20µs every 3 pulses, while we applied Vr=0.1V and tr=10µs after each pulse to measure the conductance. Figure 2 shows that device B (TiN plus Mo with thermal anneal) has good MLC with no overlap between the states while device A shows unstable MLC behavior due to time constant variation. Figure 3a shows the results of device C for the same RESET pulse train that was applied to devices A and B. Since device C switches at very low current and voltage, the conductance quantization was unstable after 15 pulses due to overstress of the conductive filament. Figure 3b shows the results after selecting the proper pulse set Vp= -0.5V and pulse width starting at tp=100ns and increasing by 100ns every 3 pulses, while we applied Vr=0.05V and tr=100ns after each pulse, resulting in a better conductance quantization compared to the previous setup. Appropriate engineering of the BE and managing of the oxygen vacancy distribution by hydrogen plasma treatment can significantly reduce the switching power.

Study of silicon-oxide RRAM devices based on complex impedance spectroscopy

This work presents a study of Metal-Oxide-Semiconductor RRAM structures with highly doped Si substrates that exhibit the resistive switching effect. We use DC measurements to show the resistance modulation effect and small-signal measurements to investigate the resistive switching layer behavior. We show that by utilizing the results of impedance spectroscopy measurements, we can build an equivalent electrical circuit that can be used to investigate the properties of RRAM devices. Our model fits very well with the measurement data for investigated frequency and voltage values.

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.

A Physics-Informed Recurrent Neural Network for RRAM Modeling

Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to enhance the modeling capabilities. The integration strategy of Verilog-A equivalent circuits, is also developed for compatibility with existing general-purpose circuit simulators. The Verilog-A model can be easily adopted into the SPICE-type simulator for the circuit design with a variable step that differs from the training process. Numerical experiments with real RRAM devices data demonstrate the feasibility and advantages of the proposed methodology.

A Flexible and Reliable RRAM-Based In-Memory Computing Architecture for Data-Intensive Applications

This paper proposes a practical, flexible, and reliable in-memory computing architecture for resistive-memory-based logic designs. Our design uses a new RRAM-based polymorphic in-memory logic gate implementing all 2-input Boolean logic functions to handle real-time applications like search engines. This design reduces the proposed architecture's power-delay product (PDP) compared to competing designs by utilizing the features of the RRAM device and the proposed reconfigurable sensing amplifier. Additionally, the simulation results on the ISCAS-89 and ITC-99 benchmarks at the 7nm technology node show that the suggested architecture's PDP is lower than the comparable state-of-the-art designs. A novel sense amplifier supporting all Boolean logic functions is also proposed to handle the applications with resistive-resistive-based inputs, such as search applications. After retrieving data from the main memory, the suggested sense amplifier computes the desired output. To assess the functionality of the suggested design in the presence of process variations, Monte Carlo simulations are conducted to authenticate the robustness and high-performance operation of the proposed design in different classes of resistive-memory-based logic in the presence of process variations. Our results further demonstrate that, compared to its counterpart, the suggested strategy dramatically reduces the energy consumption of the median, Max, and Min filters in real-world applications.

Reliable RRAM devices utilizing sol-gel derived amorphous Ce2Ti2O7 thin films

In this study, amorphous Ce2Ti2O7 (CTO) thin films were prepared on an ITO/glass substrate using the sol-gel method. The effects of film thickness, annealing temperature, post-metal annealing, and the insertion of an additional AlOx layer in between the CTO film and ITO/glass on the resistance-switching characteristics of the device were studied. All samples exhibited bipolar resistive switching (BRS) behavior. The diffusion of Al and self-formed AlOx at the Al/CTO interface were observed in samples under post-metallization annealing (PMA) treatment. The diffused Al atoms can act as dopants to provide additional oxygen vacancies, while the self-formed AlOx prevents the outward diffusion of oxygen ions. A forming-free Al/CTO/ITO sample with a switching cycle of 1565, a Vset/Vreset ratio of −1.1 V/1.0 V, an on/off ratio of ∼102, and a retention time of 104 s can be obtained after a 300 °C PMA treatment. Insertion of an AlOx layer at the CTO/ITO interface can enhance the RS performance of the device. By varying the thickness of the AlOx layer, an optimal combination of RS properties (such as switching cycles of 1926, on/off ratio of ∼103) can be obtained, making it a promising candidate for RRAM applications.

An RRAM-based building block for reprogrammable non-uniform sampling ADCs

AbstractRRAM devices have recently seen wide-spread adoption into applications such as neural networks and storage elements since their inherent non-volatility and multi-bit-capability renders them a possible candidate for mitigating the von-Neumann bottleneck. Researchers often face difficulties when developing edge devices, since dealing with sensors detecting parameters such as humidity or temperature often requires large and power-consuming ADCs. We propose a possible mitigation, namely using a RRAM device in combination with a comparator circuit to form a basic block for threshold detection. This can be expanded towards programmable non-uniform sampling ADCs, significantly reducing both area and power consumption since significantly smaller bit-resolutions are required. We demonstrate how a comparator circuit designed in 130 nm technology can be reprogrammed by programming the incorporated RRAM device. Our proposed building block consumes 83 µW.

α-Fe2O3-based artificial synaptic RRAM device for pattern recognition using artificial neural networks

We report on the α -Fe2O3-based artificial synaptic resistive random access memory device, which is a promising candidate for artificial neural networks (ANN) to recognize the images. The device consists of a structure Ag/α-Fe2O3/FTO and exhibits non-volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation, long-term depression, and spike time-dependent plasticity. In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained from α-Fe2O3 based artificial synaptic device. The proposed α-Fe2O3-based device was tested with the FMNIST and MNIST datasets and obtained a high pattern recognition accuracy of 88.06% and 97.6% test accuracy respectively. Such a high pattern recognition accuracy is attributed to the combination of the synaptic device performance as well as the novel weight mapping strategy used in the present work. Therefore, the ideal device characteristics and high ANN performance showed that the fabricated device can be useful for practical ANN implementation.

Enhanced resistive switching performance of TiO2 based RRAM device with graphene oxide inserting layer

In this work, graphene oxide (GO)/TiO2 heterostructures for resistive random access memory devices were fabricated, and the composition and microstructure of TiO2 and GO were characterized by x-ray diffraction, Raman spectroscopy, scanning electronic microscopy, and transmission electron microscopy. The resistive characteristics of the fabricated devices were investigated, and the remarkable improvement in cycle-to-cycle uniformity and high ON/OFF ratio of the TiO2 thin film-based memory device were realized by introducing a thin GO layer. The formation/rupture of the conductive filament through the migration of oxygen vacancies in the TiO2 substrate was responsible for the resistive switching. Owing to the different activation energies of reduction and oxidation of the GO, the set voltage became larger than the reset voltage. According to the linear fitting of double logarithm I–V plots, the conduction mechanism in low and high resistance states was governed by the ohmic mechanism and trap-controlled space charge limited current, respectively. The oxygen migration-induced oxidation/reduction in GO rendered it a good oxygen vacancy reservoir, which is responsible for the enhanced cycle-to-cycle uniformity and high ON/OFF ratio.

Resistive Switching Characteristic of Cu Electrode-Based RRAM Device

The conductive bridge random access memory (CBRAM) device has been widely studied as a promising candidate for next-generation nonvolatile memory applications, where Cu as an electrode plays an important role in the resistive switching (RS) process. However, most studies only use Cu as one electrode, either the top electrode (TE) or the bottom electrode (BE); it is rarely reported that Cu is used as both TE and BE at the same time. In this study, we fabricated CBRAM devices by using Cu as both the TE and BE, and studied the RS characteristic of these devices. With Al2O3 as the switching layer (5~15 nm), the devices showed good bipolar RS characteristics. The endurance of the device could be as high as 106 cycles and the retention time could be as long as 104 s. The Al2O3 thickness influences the bipolar RS characteristic of the devices including the initial resistance, the forming process, endurance, and retention performance. The Cu electrode-based RRAM devices also present negative bias-suppressed complementary resistive switching (CRS) characteristics, which makes it effective to prevent the sneak path current or crosstalk problem in high-density memory array circuits.

Bipolar Switching Properties of the Transparent Indium Tin Oxide Thin Film Resistance Random Access Memories.

In this study, the bipolar switching properties and electrical conduction behaviors of the ITO thin films RRAM devices were investigated. For the transparent RRAM devices structure, indium tin oxide thin films were deposited by using the RF magnetron sputtering method on the ITO/glass substrate. For the ITO/ITOX/ITO/glass (MIM) structure, an indium tin oxide thin film top electrode was prepared to form the transparent RRAM devices. From the experimental results, the 102 On/Off memory ratio and bipolar switching cycling properties for set/reset stable states were found and discussed. All transparent RRAM devices exhibited the obvious memory window and low set voltage for the switching times of 120 cycles. The electrical transport mechanisms were dominated by the ohmic contact and space charge limit conduction (SCLC) models for set and reset states. Finally, the transmittances properties of the transparent ITO/ITOX/ITO RRAM devices for the different oxygen growth procedures were about 90% according to the UV-Vis spectrophotometer for the visible wavelength range.

An RRAM-Based Digital Computing-in-Memory Macro With Dynamic Voltage Sense Amplifier and Sparse-Aware Approximate Adder Tree

RRAM is a promising candidate to implement large-capacity in-memory computing on edge AI devices due to its high density. However, the efficiency and accuracy of RRAM-based computing-in-memory (CIM) works are limited by large accumulation currents and device variations. The SRAM-based digital CIM achieves superior performance and efficiency while the adder tree dominates the area. In this brief, a digital RRAM CIM macro is proposed to achieve a better trade-off between accuracy, energy, and performance by three techniques. First, a dynamic voltage sense amplifier is designed to reduce >90% read currents of low resistance state (LRS) cell. Second, an OR gate approximate adder tree is proposed to reduce the area of the adder tree by 40%. Third, a sparse-aware finetuning algorithm is proposed to reduce the accuracy loss of approximate arithmetic to 0.4% on Cifar-10 dataset. The proposed design achieves 1966TOPS/W energy efficiency and 51.2GOPS/Kb normalized throughput at 1-bit precision which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> higher than previous RRAM-based designs. This brief demonstrates the advantage of digital CIM using RRAM devices.

Forming-free RRAM device based on HfO2 thin film for non-volatile memory application using E-beam evaporation method

Forming-free RRAM device based on HfO2 thin film for non-volatile memory application using E-beam evaporation method