Resistive Switching Random Access Memory Research Articles

Negative differential resistance effect induced by bulk and interface defect states in Pt/CeO2-x/Pt resistive memory devices

Resistive switching (RS) random access memory (ReRAM) has been paid great attention for next-generation data storage technology because of its advantages of nonvolatile behavior, low power consumption, high density, rapid writing/erasing speed, and simple structure. In this work, we fabricated Pt/CeO2-x/Pt resistive memory device by spin-coated method. The RS behavior is demonstrated. And the negative differential resistance (NDR) effect was observed during the set processes. By comparing with current magnitude of NRD effect reported in the literature, we construct and analyze the interface trap and deep level defect models. This work opens up a new view for insight of the influence of defect on ReRAM performance.

Uniform, fast, and reliable CMOS compatible resistive switching memory

Resistive switching random access memory (RRAM) is considered as one of the potential candidates for next-generation memory. However, obtaining an RRAM device with comprehensively excellent performance, such as high retention and endurance, low variations, as well as CMOS compatibility, etc., is still an open question. In this work, we introduce an insert TaO x layer into HfO x -based RRAM to optimize the device performance. Attributing to robust filament formed in the TaO x layer by a forming operation, the local-field and thermal enhanced effect and interface modulation has been implemented simultaneously. Consequently, the RRAM device features large windows (> 103), fast switching speed (~ 10 ns), steady retention (> 72 h), high endurance (> 108 cycles), and excellent uniformity of both cycle-to-cycle and device-to-device. These results indicate that inserting the TaO x layer can significantly improve HfO x -based device performance, providing a constructive approach for the practical application of RRAM.

Online Fault Detection in ReRAM-Based Computing Systems for Inferencing

A resistive switching random access memory (ReRAM)-based computing system (RCS) provides an energy-efficient hardware implementation of vector–matrix multiplication for machine-learning hardware. However, it is susceptible to faults due to the immature resistive switching random access memory (ReRAM) fabrication process. We propose an efficient online fault-detection method for RCS. This method monitors the dynamic power consumption of each ReRAM crossbar and determines the occurrence of faults when a changepoint is detected in the monitored power-consumption time series. To estimate the percentage of faulty cells in a faulty ReRAM crossbar, we compute statistical features immediately before and after the changepoint and use them as independent variables; we use the percentage of faulty cells as dependent variables to train a predictive model using machine learning. In this way, the computationally expensive fault localization and error-recovery steps are carried out only when a high fault rate is estimated. Simulation results show that, with the fault-detection method and the predictive model, the test time is significantly reduced, the hardware overhead is negligible, and high classification accuracy for the MNIST and CIFAR-10 datasets using RCS can still be ensured.

In-Memory Principal Component Analysis by Crosspoint Array of Resistive Switching Memory: A new hardware approach for energy-efficient data analysis in edge computing

In-Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing.

CMOS-compatible wafer-scale Si subulate array for superb switching uniformity of RRAM with localized nanofilaments

Resistive switching random access memory (RRAM) is one of the most promising candidates with high-density three-dimensional integration characteristics for next-generation nonvolatile memory technology. However, the poor uniformity issue caused by the stochastic property of the conductive filament (CF) impedes the large-scale manufacture of RRAM chips. Subulate array has been introduced into the RRAM to minimize the CF randomness, but the methods are cumbersome, expensive, or resolution-limited for large-scale preparation. In this work, Si subulate array (SSA) substrates with different curvature radii prepared by a wafer-scale and nanoscale-controllable method are introduced for RRAM fabrication. The SSA structure, which induces a quasi-single CF or a few CFs formed in the tip region (TR) of the device as evidenced by the high-resolution transmission electron microscopy and energy dispersive spectroscopy characterization, dramatically improves the cycle-to-cycle and device-to-device uniformity. Decreasing the curvature radius of the TR significantly improves the device performance, including switching voltages, high/low resistance states, and retention characteristics. The improved uniformity can be attributed to the enhanced local electric field in the TR. The proposed SSA provides a low-cost, uniform, CMOS-compatible, and nanoscale-controllable optimization strategy for the large-scale integration of highly uniform RRAM devices.

Endurance of 2 Mbit Based BEOL Integrated ReRAM

In this work, we experimentally characterize the endurance of 2 Mbit resistive switching random access memories (ReRAMs) from a 16 MBit test-chip. Here, very rare failure events where the memory cells become stuck in the low-resistive state (LRS) are observed. As this failure mechanism is the limiting one concerning the endurance of this ReRAM implementation, extensive investigations are conducted and presented. The experimental findings are detailed via a voltage divider model, illustrating why memory cells can become stuck in the LRS. It is proposed, that an insufficient voltage dropping over the cell due to an unfavorable combination of cell- and transistor resistances is responsible for stuck-at-LRS bits. Furthermore, we give predictions for the origin of these suboptimal combinations. Additionally, a one-dimensional Kinetic Monte Carlo (KMC) model that allows a statistical investigation of large numbers of cells with regard to rare random events has been developed. Here, we fortify our proposed explanation for the observed failure mechanism by the simulation and evaluation of the switching process of the memory. All simulations are in very good agreement with the experimental data. Finally, based on our findings, we give suggestions for the improvement of switching algorithms.

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental reliability characteristics of the resistive-switching memory cell. Also, the superiority of this programming method using the proposed RRAM compact model is demonstrated. In addition, a one weight/one synaptic device structure is newly adopted for realizing high-density synapse arrays by using a nonnegative weight constraint in supervised learning. Finally, the pattern recognition accuracies obtained at the software and hardware levels are compared.

Solution-Processed Natural Konjac Glucomannan Material for Resistive Switching Memory

Natural konjac glucomannan (KGM)-based resistive random access memory (RRAM) devices using a simple solution process were illustrated. The memory properties of the KGM-based resistive layer under study were determined by the relationship between the physical properties of KGM thin film and the performance of RRAM. The memory properties in the KGM thin films with four different top electrodes (Ti, Al, Au, and Ni) and ITO as bottom electrodes were also investigated, depending on their different work function differences. The optimized Al/KGM/ITO structure with AlOx interfacial layer was between Al electrode and the KGM film, which produced good resistive-switching properties in the order of 100 cycles with uniform DC cyclic efficiency and a good retention ability of more than 104 s at room temperature. In addition, the surface of KGM thin films to decrease the baking temperature with KGM content of 1% induced the sp3 hybridization of carbon; hence, a simple method for fabricating a RRAM device with high ON/OFF ratio (>104) was provided. Energy-dispersive X-ray spectroscopy line-scan analyses demonstrated that the formation and rupture of carbon-based filament paths, the dominant mechanism in resistive switching, can be construed. This work helps in understanding the resistive switching of KGM-based RRAM and maintaining stable operations, which are important in increasing the application of KGM in electronic devices.

Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning

Nowadays, artificial neural networks (ANNs) can outperform the human brain’s ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation, and consolidation typical of biological organisms. Here, we present a hardware design based on arrays of SiO x resistive switching random-access memory (RRAM), which allows combining the accuracy of convolutional neural networks with the flexibility of bio-inspired neuronal plasticity. In order to enable the combination of the stable and the plastic attributes of the network, we exploit the spike-frequency adaptation of the neurons relying on the multilevel programming of the RRAM devices. This procedure enhances the efficiency and accuracy of the network for MNIST, noisy MNIST (N-MNIST), Fashion-MNIST, and CIFAR-10 datasets, with inference accuracies of about 99%–89%, respectively. We also demonstrate that the hardware is capable of asynchronous self-adaptation of its operative frequency according to the fire rate of the spiking neuron, thus optimizing the whole behavior of the network. We finally show that the system enables fast and accurate filter retraining to overcome catastrophic forgetting, showing high efficiency in terms of operations per second and robustness against device non-idealities. This work paves the way for the theoretical modeling and hardware realization of resilient autonomous systems in dynamic environments.

Switching characteristic of fabricated nonvolatile bipolar resistive switching memory (ReRAM) using PEDOT: PSS/GO

In this study, a bipolar resistive switching random access memory cell (ReRAM) by blending PEDOT: PSS into Graphene Oxide (GO) with 1:1 wt% is reported. To compare the switching mechanism, the memory devices based on GO and PEDOT: PSS are distinctly fabricated and characterized too. The spin coated active layers are sandwiched between ITO and Al as the bottom and top electrodes, respectively. The resistive switching mechanism in PEDOT: PSS/GO memory is based on filamentary type. The SET and RESET operation of the PEDOT: PSS/GO memory is relatively symmetric but GO and PEDOT: PSS samples have a significant asymmetric hysteresis curve. The carrier transport mechanism of the bistable behaviour for the fabricated non-volatile ReRAM is described based on space charge limited current (SCLC). In our experiments, the structure ITO/PEDOT:PSS-GO/Al demonstrated good switching behaviour with approximately 100 on/off current ratio, 3.5 V set/reset voltage and, a high retention time of 6000 s. In addition, the crystallinity and surface topography of the deposited thin film was characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy (SEM). The simple structure and low-cost fabrication offer embedding in data storage and computing application as future electronics and printed electronics.

Halide Perovskites for Resistive Switching Memory.

Resistive switching random access memory (RRAM), also known as memristor, is regarded as an emerging nonvolatile memory and computing-in-memory technology to address the intrinsic physical limitations of conventional memory and the bottleneck of von Neumann architecture. In particular, halide perovskite RRAMs have attracted widespread attention in recent years because of their ionic migration nature and excellent photoelectric properties. This Perspective first provides a condensed overview of halide perovskite RRAMs based on materials, device performance, switching mechanism, and potential applications. Moreover, this Perspective attempts to detail the challenges, such as the quality of halide perovskite films, the compatible processing of device fabrication, the reliability of memory performance, and clarification of the switching mechanism, and further discusses how the outstanding challenges of halide perovskite RRAMs could be met in future research.

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices.

Major challenges concerning the reliability of resistive switching random access memories based on the valence change mechanism (VCM) are short-term instability and long-term retention failure of the programmed resistance state, particularly in the high resistive state. On the one hand, read noise limits the reliability of VCMs via comparatively small current jumps especially when looking at the statistics of millions of cells that are needed for industrial applications. Additionally, shaping algorithms aiming for an enlargement of the read window are observed to have no lasting effect. On the other hand, long-term retention failures limiting the lifetime of the programmed resistance states need to be overcome. The physical origin of these phenomena is still under debate and needs to be understood much better. In this work, we present a three-dimensional kinetic Monte Carlo simulation model where we implemented diffusion-limiting domains to the oxide layer of the VCM cell. We demonstrate that our model can explain both instability and retention failure consistently by the same physical processes. Further, we find that the random diffusion of oxygen vacancies plays an important role regarding the reliability of VCMs and can explain instability phenomena as the shaping failure as well as the long-term retention failure in our model. Additionally, the results of the simulations are compared with experimental data of read noise and retention investigations on ZrO2-based VCM devices.

Switching Dynamics of Ag-Based Filamentary Volatile Resistive Switching Devices—Part I: Experimental Characterization

Volatile resistive switching random access memory (RRAM) devices are drawing attention in both storage and computing applications due to their high ON-/ OFF-ratio, fast switching speed, low leakage, and scalability. However, these devices are relatively new and the physical switching mechanisms are still under investigation. A thorough understanding and modeling of the physical dynamics underlying filament formation and self-dissolution are of utmost importance in view of future integration of volatile devices in neuromorphic systems and in memory arrays. To assess the physical mechanisms and develop appropriate models, though, the electrical properties of the device have to be characterized. In this article, we present an extensive study of Ag/SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> -based volatile RRAM devices. Important parameters, such as switching time, switching voltage, and retention time are investigated as a function of the stimulation conditions. A physical explanation is provided and the applicability of the device in neuromorphic systems is discussed.

Switching Dynamics of Ag-Based Filamentary Volatile Resistive Switching Devices—Part II: Mechanism and Modeling

Understanding the switching mechanism of the volatile resistive switching random access memory (RRAM) device is important to harness its characteristics and further enhance its performance. Accurate modeling of its dynamic behavior is also of deep value for its applications both as selector and as short-term memory synapse for future neuromorphic applications operating in temporal domain. In this work, we investigate the switching and retention (relaxation) processes of the Ag-based metallic filamentary volatile resistive switching devices. We find that the switching process can be modeled by the ionic drift under electric field, while the retention process can be modeled by the ionic diffusion along the filament surface driven by the gradient of surface atomic concentration. Through further theoretical analysis, we also find that the ionic drift and ionic diffusion can be unified within the general Einstein relation. To confirm this relation, we collect ionic mobility and diffusivity data from the literature using the switching and retention model. Finally, we show that the read voltage dependent retention time can be explained by the competition between the ionic drift and diffusion flux.

A More Hardware-Oriented Spiking Neural Network Based on Leading Memory Technology and Its Application With Reinforcement Learning

In recent days, more hardware-driven artificial intelligence system capable of brain-like low-energy consumption is gaining ever-increasing interest. The hardware-driven property lies in the low-power synaptic device and its array along with the area and energy-efficient neuron circuits. In this work, a spiking neural network (SNN) based on analog synaptic device of resistive-switching random access memory (RRAM) is constructed from the experimentally fabricated devices. Furthermore, the capability of the designed SNN hardware for sequential tasks through an optimal reinforcement learning (RL) algorithm is demonstrated. More specifically, the Rush Hour game is conducted as an example of applications for the sequential task for which an SNN architecture is plausibly suited. The rule of the game is simple but has not been demonstrated by a hardware-oriented artificial neural network (ANN) yet, and in this work, it is reported that the analog RRAM synaptic devices in the cross-point array architecture successfully solve the problem via the RL algorithm.

Electroforming-Free Bipolar Resistive Switching Memory Based on Magnesium Fluoride.

Electroforming-free resistive switching random access memory (RRAM) devices employing magnesium fluoride (MgFx) as the resistive switching layer are reported. The electroforming-free MgFx based RRAM devices exhibit bipolar SET/RESET operational characteristics with an on/off ratio higher than 102 and good data retention of >104 s. The resistive switching mechanism in the Ti/MgFx/Pt devices combines two processes as well as trap-controlled space charge limited conduction (SCLC), which is governed by pre-existing defects of fluoride vacancies in the bulk MgFx layer. In addition, filamentary switching mode at the interface between the MgFx and Ti layers is assisted by O–H group-related defects on the surface of the active layer.

Nonideal resistive and synaptic characteristics in Ag/ZnO/TiN device for neuromorphic system

Ideal resistive switching in resistive random-access memory (RRAM) should be ensured for synaptic devices in neuromorphic systems. We used an Ag/ZnO/TiN RRAM structure to investigate the effects of nonideal resistive switching, such as an unstable high-resistance state (HRS), negative set (N-set), and temporal disconnection, during the set process and the conductance saturation feature for synaptic applications. The device shows an I–V curve based on the positive set in the bipolar resistive switching mode. In 1000 endurance tests, we investigated the changes in the HRS, which displays large fluctuations compared with the stable low-resistance state, and the negative effect on the performance of the device resulting from such an instability. The impact of the N-set, which originates from the negative voltage on the top electrode, was studied through the process of intentional N-set through the repetition of 10 ON/OFF cycles. The Ag/ZnO/TiN device showed saturation characteristics in conductance modulation according to the magnitude of the applied pulse. Therefore, potentiation or depression was performed via consecutive pulses with diverse amplitudes. We also studied the spontaneous conductance decay in the saturation feature required to emulate short-term plasticity.

Review on resistive switching mechanisms of bio-organic thin film for non-volatile memory application

Abstract Bio-organic, as one of the sustainable and bioresorbable materials, has been used as an active thin film in producing resistive switching random access memory (RRAM) due to its specialized properties. This type of nonvolatile memory consists of a simple unit structure with the processed and solidified bio-organic-based thin film sandwiched between two electrodes. Its memory characteristics are significantly affected by the resistive-switching mechanism. However, to date, the reported mechanisms are very diverse and scattered, and to our best knowledge, there is no literature that reviewed comprehensively the mechanisms of resistive switching in bio-organic-based thin films. Therefore, the objective of this article is to critically analyze data related to the mechanisms of the bio-organic-based RRAM since it was first reported. Based on the pool of literature, three types of mechanisms are categorized, namely electronic, electrochemical, and thermochemical, and the naming is well justified based on the principle of operation. The determining factors and roles of bio-organic material and the two electrodes in governing the three mechanisms have been analyzed, reviewed, discussed, and compared.

Resistive switching characteristics of epitaxial NiO thin films affected by lattice strains and external forces

We investigated the resistive switching characteristics of NiO films grown epitaxially on two single-crystal Rh and Ni substrates. Two epitaxial NiO films were relatively small compressive strained on the Rh substrate, and confirmed by the x-ray diffraction experiments that the strain was relatively large compressive on the Ni substrate. Comparing the forming, SET, and RESET voltages of NiO resistive switching random access memory capacitors, NiO films grown on the Ni substrates showed relatively higher voltages than Rh substrates. We propose that this compressive strain has an effect on raising the forming, SET, and RESET voltages. Furthermore, we observed that the forming voltage decreases while reducing the compressive strain by applying pressure to the NiO thin film grown on the Ni substrate with an atomic force microscope tip. In addition, we showed that relaxation of compressive strain by external force by first-principles calculations can reduce the oxygen deficiency energy and lower the forming voltage and working voltages, which supports the experimental results well.

Effective control of filament efficiency by means of spacer HfAlOx layers and growth temperature in HfO2 based ReRAM devices

Resistive switching random access memories are being thoroughly studied as prospective non-volatile memories. In this paper, we report electrical characterization of HfO2:Al2O3based metal-insulator–metal structures devised using atomic layer deposition. Dependences of electrical behavior on HfO2:Al2O3 cycle ratio is studied. An explanation for the differences between the Resistive Switching properties of the samples is proposed, based on the distribution of HfAlOx layers of the sample. Dependence of the RS properties of the samples on their growth temperature is discussed.