Multi-level control of conductive nano-filament evolution in HfO2 ReRAM by pulse-train operations

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO 2 based memristive device. 1 st and 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼ 4.3 × and ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab's neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

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.

Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications

As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO₂-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.

The Impact of Trap-Assisted Tunneling and Poole–Frenkel Emission on Synaptic Potentiation in an α-Fe2O3/p-Si Memristive Device

A signature of synaptic potentiation conductance has been observed in an α-Fe2O3/p-Si device fabricated using spin coating. The conductance of the device in dark conditions and illumination with a white light source was characterized as a function of the application of a periodic bias (voltage) with a triangular profile. The conductance of the device increases with the number of voltage cycles applied and plateaus to its maximum value of 0.70 μS under dark conditions and 12.00 μS under illumination, and this mimics the analog synaptic weight change with the action potential of a neuron. In the range of applied voltage from 0 V to 0.7 V, the conduction mechanism corresponds to trap-assisted tunneling (TAT) and in the range of 0.7–5 V it corresponds to the Poole–Frenkel emission (PFE). The conductance as a function of electrical pulses was fitted with a Hill function, which is a measure of cooperation in biological systems. In this case, it allows one to determine the turn-on threshold (K) of the device in terms of the number of voltage pulses, which are found to be 3 and 166 under dark and illumination conditions, respectively. The gradual conductance change and activation after a certain number of pulses perfectly mimics the synaptic potentiation of neurons. In addition, the threshold parameter extracted from the Hill equation fit, acting as the number of pulses for synaptic activation, is found to have programmability with the intensity of the light illumination.

A high throughput generative vector autoregression model for stochastic synapses

By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for large-scale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real-world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.

Conductance-Aware Quantization Based on Minimum Error Substitution for Non-Linear-Conductance-State Tolerance in Neural Computing Systems

Emerging resistive random-access memory (ReRAM) has demonstrated great potential in the achievement of the in-memory computing paradigm to overcome the well-known “memory wall” in current von Neumann architecture. The ReRAM crossbar array (RCA) is a promising circuit structure to accelerate the vital multiplication-and-accumulation (MAC) operations in deep neural networks (DNN). However, due to the nonlinear distribution of conductance levels in ReRAM, a large deviation exists in the mapping process when the trained weights that are quantized by linear relationships are directly mapped to the nonlinear conductance values from the realistic ReRAM device. This deviation degrades the inference accuracy of the RCA-based DNN. In this paper, we propose a minimum error substitution based on a conductance-aware quantization method to eliminate the deviation in the mapping process from the weights to the actual conductance values. The method is suitable for multiple ReRAM devices with different non-linear conductance distribution and is also immune to the device variation. The simulation results on LeNet5, AlexNet and VGG16 demonstrate that this method can vastly rescue the accuracy degradation from the non-linear resistance distribution of ReRAM devices compared to the linear quantization method.

Improved analog switching characteristics of Ta2O5-based memristor using indium tin oxide buffer layer for neuromorphic computing

A memristor is defined as a non-volatile memory switching two-terminal resistor, and a memristor with digital switching characteristics is widely studied as a next-generation non-volatile memory because of its simple structure, high integration density, and low power consumption. Recently, analog memristors with gradual resistance switching (RS) characteristics have garnered great attention because of their potential to implement artificial synapses that can emulate the brain functions. Transition metal oxides are thought to be strong candidate materials for the RS. In particular, tantalum oxide (TaO_x)-based memristive devices provide stable and durable switching characteristics. TaO_x-based memristors utilize analog switching characteristics and have excellent durability and reliability, so they can be applied as artificial synaptic device. In this study, the characteristics of analog RS using Ta₂O₅-based memristive devices were investigated. The current level of the Pt/Ta₂O₅/Pt memristors was improved by adjusting the thickness of Ta₂O₅. In particular, when an indium-tin-oxide (ITO) buffer layer was added to Ta₂O₅forming a Pt/ITO/Ta₂O₅/Pt heterostructured double-layer device, it showed more symmetrical potentiation and depression characteristics under both polarities than a single-layer device without ITO layer. The symmetrical and linear potentiation and depression characteristics are essential for the development of efficient memristor-based neuromorphic systems. Insertion of the ITO buffer layer improves linearity, symmetry, and stability of the analog RS properties of Ta₂O₅-based memristors to artificial synapses.

Operando Direct Observation of Filament Formation in Resistive Switching Devices Enabled by a Topological Transformation Molecule

Conductive filaments (CFs) play a critical role in the mechanism of resistive random-access memory (ReRAM) devices. However, in situ detection and visualization of the precise location of CFs are still key challenges. We demonstrate for the first time the use of a π-conjugated molecule which can transform between its twisted and planar states upon localized Joule heating generated within filament regions, thus reflecting the locations of the underlying CFs. Customized patterns of CFs were induced and observed by the π-conjugated molecule layer, which confirmed the hypothesis. Additionally, statistical studies on filaments distribution were conducted to study the effect of device sizes and bottom electrode heights, which serves to enhance the understanding of switching behavior and their variability at device level. Therefore, this approach has great potential in aiding the development of ReRAM technology.

Asymmetric Bipolar Resistive Switching of Halide Perovskite Film in Contact with TiO2 Layer

Halide perovskite materials such as methylammonium lead iodide (CH₃NH₃PbI₃) have attracted considerable interest for the resistive random-access memory applications, which exploit a dramatic change in the resistance by an external electric bias. In many semiconductor films, the drift, accumulation, and chain formation of defects explain the change in the resistance by an external bias. This study demonstrates that the interface of CH₃NH₃PbI₃ with TiO₂ has a significant impact on the formation and rupture of defect chains and causes the asymmetric bipolar resistive switching in the Au/CH₃NH₃PbI₃/TiO₂/FTO device (FTO = fluorine-doped tin oxide). When a negative bias is applied to the Au electrode, iodine interstitials with the lowest migration activation energy move toward TiO₂ in the CH₃NH₃PbI₃ layer and pile up at the CH₃NH₃PbI₃-TiO₂ interface. Under the same condition, oxygen vacancies in the TiO₂ layer also travel to the CH₃NH₃PbI₃-TiO₂ interface and strongly attract iodine interstitials. As a result, a Schottky barrier appears at the CH₃NH₃PbI₃-TiO₂ interface, and the resistance of Au/CH₃NH₃PbI₃/TiO₂/FTO becomes much larger than that of Au/CH₃NH₃PbI₃/FTO in the high resistance state. The frequency dependence of the capacitance confirms the asymmetric appearance of a large space charge polarization at the CH₃NH₃PbI₃-TiO₂ interface, which causes the unique bipolar resistive switching behavior with the on/off ratio (10³) and retention time (>10⁴ seconds) at -0.85 V in Au/CH₃NH₃PbI₃/TiO₂/FTO film.

Multi-level characteristics of TiOx transparent non-volatile resistive switching device by embedding SiO2 nanoparticles

TiOx-based resistive switching devices have recently attracted attention as a promising candidate for next-generation non-volatile memory devices. A number of studies have attempted to increase the structural density of resistive switching devices. The fabrication of a multi-level switching device is a feasible method for increasing the density of the memory cell. Herein, we attempt to obtain a non-volatile multi-level switching memory device that is highly transparent by embedding SiO2 nanoparticles (NPs) into the TiOx matrix (TiOx@SiO2 NPs). The fully transparent resistive switching device is fabricated with an ITO/TiOx@SiO2 NPs/ITO structure on glass substrate, and it shows transmittance over 95% in the visible range. The TiOx@SiO2 NPs device shows outstanding switching characteristics, such as a high on/off ratio, long retention time, good endurance, and distinguishable multi-level switching. To understand multi-level switching characteristics by adjusting the set voltages, we analyze the switching mechanism in each resistive state. This method represents a promising approach for high-performance non-volatile multi-level memory applications.

Multi-Level Switching of Al-Doped HfO2 RRAM with a Single Voltage Amplitude Set Pulse

In this paper, the resistive switching characteristics in a Ti/HfO2: Al/Pt sandwiched structure are investigated for gradual conductance tuning inherent functions. The variation in conductance of the device under different amplitudes and voltage pulse widths is studied. At the same time, it was found that the variation in switching parameters in resistive random-access memory (RRAM) under impulse response is impacted by the initial conductance states. The device was brought to a preset resistance value range by energizing a single voltage amplitude pulse with a different number of periodicities. This is an efficient and simple programming algorithm to simulate the strength change observed in biological synapses. It exhibited an on/off of about 100, an endurance of over 500 cycles, and a lifetime (at 85 °C) of around 105 s. This multi-level switching two-terminal device can be used for neuromorphic applications to simulate the gradual potentiation (increasing conductance) and inhibition (decreasing conductance) in an artificial synapse.

Ratio-based multi-level resistive memory cells

Ratio-based encoding has recently been proposed for single-level resistive memory cells, in which the resistance ratio of a pair of resistance-switching devices, rather than the resistance of a single device (i.e. resistance-based encoding), is used for encoding single-bit information, which significantly reduces the bit error probability. Generalizing this concept for multi-level cells, we propose a ratio-based information encoding mechanism and demonstrate its advantages over the resistance-based encoding for designing multi-level memory systems. We derive a closed-form expression for the bit error probability of ratio-based and resistance-based encodings as a function of the number of levels of the memory cell, the variance of the distribution of the resistive states, and the ON/OFF ratio of the resistive device, from which we prove that for a multi-level memory system using resistance-based encoding with bit error probability x, its corresponding bit error probability using ratio-based encoding will be reduced to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x^2$$\end{document}x2 at the best case and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x^{\sqrt{2}}$$\end{document}x2 at the worst case. We experimentally validated these findings on multiple resistance-switching devices and show that, compared to the resistance-based encoding on the same resistive devices, our approach achieves up to 3 orders of magnitude lower bit error probability, or alternatively it could reduce the cell’s programming time and programming energy by up 5–10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, while achieving the same bit error probability.

Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network applications

Although good performance has been reported in shallow neural networks, the application of memristor synapses towards realistic deep neural networks has met more stringent requirements on the synapse properties, particularly the high precision and linearity of the synaptic analog weight tuning. In this study, a LiAlOX memristor synapse was fabricated and optimized to address these demands. By delicately tuning the initial conductance states, 120-level continuously adjustable conductance states were obtained and the nonlinearity factor was substantially reduced from 8.96 to 0.83. The significant enhancements were attributed to the reduced Schottky barrier height (SBH) between the filament tip and the electrode, which was estimated from the measured I-V curves. Furthermore, a deep neural network for realistic action recognition task was constructed, and the recognition accuracy was found to be increased from 15.1% to 91.4% on the Weizmann video dataset by adopting the above-described device optimization method.

Perovskite neural trees

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses.

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications

In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed. First, a brief overview of the field of emerging memory technologies is provided. The material properties, resistance switching mechanism, and electrical characteristics of RRAM are discussed. Also, various issues such as endurance, retention, uniformity, and the effect of operating temperature and random telegraph noise (RTN) are elaborated. A discussion on multilevel cell (MLC) storage capability of RRAM, which is attractive for achieving increased storage density and low cost is presented. Different operation schemes to achieve reliable MLC operation along with their physical mechanisms have been provided. In addition, an elaborate description of switching methodologies and current voltage relationships for various popular RRAM models is covered in this work. The prospective applications of RRAM to various fields such as security, neuromorphic computing, and non-volatile logic systems are addressed briefly. The present review article concludes with the discussion on the challenges and future prospects of the RRAM.

Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device

In this study, by inserting a buffer layer of TiO_x between the SiO_x:Ag layer and the bottom electrode, we have developed a memristor device with a simple structure of Ag/SiO_x:Ag/TiO_x/p⁺⁺-Si by a physical vapor deposition process, in which the filament growth and rupture can be efficiently controlled during analog switching. The synaptic characteristics of the memristor device with a wide range of resistance change for weight modulation by implementing positive or negative pulse trains have been investigated extensively. Several learning and memory functions have been achieved simultaneously, including potentiation/depression, paired-pulse-facilitation (PPF), short-term plasticity (STP), and STP-to-LTP (long-term plasticity) transition controlled by repeating pulses more than a rehearsal operation, and spike-time-dependent-plasticity (STDP) as well. Based on the analysis of logarithmic I-V characteristics, it has been found that the controlled evolution/dissolution of conductive Ag-filaments across the dielectric layers can improve the performance of the testing memristor device.

Memristive and CMOS Devices for Neuromorphic Computing

Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

Applications of Nanodiamonds in the Detection and Therapy of Infectious Diseases

We are constantly exposed to infectious diseases, and they cause millions of deaths per year. The World Health Organization (WHO) estimates that antibiotic resistance could cause 10 million deaths per year by 2050. Multidrug-resistant bacteria are the cause of infection in at least one in three people suffering from septicemia. While antibiotics are powerful agents against infectious diseases, the alarming increase in antibiotic resistance is of great concern. Alternatives are desperately needed, and nanotechnology provides a great opportunity to develop novel approaches for the treatment of infectious diseases. One of the most important factors in the prognosis of an infection caused by an antibiotic resistant bacteria is an early and rigorous diagnosis, jointly with the use of novel therapeutic systems that can specifically target the pathogen and limit the selection of resistant strains. Nanodiamonds can be used as antimicrobial agents due to some of their properties including size, shape, and biocompatibility, which make them highly suitable for the development of efficient and tailored nanotherapies, including vaccines or drug delivery systems. In this review, we discuss the beneficial findings made in the nanodiamonds field, focusing on diagnosis and treatment of infectious diseases. We also highlight the innovative platform that nanodiamonds confer for vaccine improvement, drug delivery, and shuttle systems, as well as their role in the generation of faster and more sensitive clinical diagnosis.

Bio-inspired synthesis of mesoporous HfO2 nanoframes as reactors for piezotronic polymerization and Suzuki coupling reactions

Complex nanostructures with high compositional and structural tailorability are highly desired in order to meet the material needs in the rapid development of nanoscience and nanotechnology. Therefore, the synthetic technique is of essential importance but currently still suffers from many challenges. Herein, we elaborately explore and demonstrate the flexibility of the anisotropic metallo-organic compound (dihafnium dichloride, Cp2HfCl2) for the fabrication of inorganic architectures by mimicking the assembly behaviors in biomolecules. The open and discrete architectures of mesoporous HfO2 nanoframes were constructed via the self-assembly of precursor with acetone as solvent and ammonia as the basic source, but without any addition of auxiliary organic molecules, like surfactants, DAN or peptides. In addition, the nanostructures (hollow spheres, solid spheres, yolk-shells, aggregations and defect-rich nanoparticles) of HfO2 assemblies can be well manipulated by simply modulating the synthesis parameters. The marked difference in the chemical bonds by the different ligands resulted in discrepant hydrolysis and then specific directional bonds for the diversity of the resultant HfO2 assemblies. Interestingly, the HfO2 nanoframe exhibits enhanced piezoelectricity, and can be used as a microelectrode reactor to trigger the pseudo-electrochemical aniline polymerization reaction by introducing ultrasonic excitation to renew the surface charges. Moreover, as compared with nanoparticle catalysts, the palladium (Pd) loaded nanoframe reactor exhibits obvious enhanced catalytic performance for classical Suzuki coupling, benefiting from the structural advantages of the HfO2 frame. Our findings here can be expected to offer new perspectives to find suitable materials by understanding the analogy between materials chemistry and biomolecule chemistry.

Fully "Erase-free" Multi-Bit Operation in HfO2-Based Resistive Switching Device

Fully "Erase-free" multi-bit operation was demonstrated in a W/HfO₂/TiN-stacked resistive switching device. The term Erase-free means that a digital state in a multi-bit operation can be achieved without initializing the device resistance state when the device moves to another digital state. Because initializing the resistance state of a resistive switching device causes high energy consumption, omitting this sequence can achieve energy efficient multi-bit operation during rewriting of the resistance state of the device. Experimentally, an operational energy savings of up to 75% was confirmed. For stable and reliable Erase-free operation, several prerequisites are required, such as gradual resistance change with electric pulse stimuli during both writing and erasing, predictable operational voltages for certain resistance states, and high reliability of resistive switching. These prerequisites could be achieved by adopting a W top electrode in a W/HfO₂/TiN-stacked resistive switching device. These results can pave the way to future nonvolatile memory applications.

Scaling Effect on Silicon Nitride Memristor with Highly Doped Si Substrate

A feasible approach is reported to reduce the switching current and increase the nonlinearity in a complementary metal-oxide-semiconductor (CMOS)-compatible Ti/SiN_x /p⁺ -Si memristor by simply reducing the cell size down to sub-100 nm. Even though the switching voltages gradually increase with decreasing device size, the reset current is reduced because of the reduced current overshoot effect. The scaled devices (sub-100 nm) exhibit gradual reset switching driven by the electric field, whereas that of the large devices (≥1 µm) is driven by Joule heating. For the scaled cell (60 nm), the current levels are tunable by adjusting the reset stop voltage for multilevel cells. It is revealed that the nonlinearity in the low-resistance state is attributed to Fowler-Nordheim tunneling dominating in the high-voltage regime (≥1 V) for the scaled cells. The experimental findings demonstrate that the scaled metal-nitride-silicon memristor device paves the way to realize CMOS-compatible high-density crosspoint array applications.

Resistive switching of the HfOx/HfO2 bilayer heterostructure and its transmission characteristics as a synapse

In this work, HfO_x/HfO₂ homo-bilayer structure based resistive random access memory devices were fabricated, and the resistive switching characteristics of the devices were investigated. The samples with an Ar/O₂ ratio of 12 : 2 exhibited improved switching performance including better uniformity, endurance and retention, which was selected to imitate the “learning” and “forgetting” function of biological synapses. The multilevel conductance of the HfO_x/HfO₂ homo-bilayer structure under the model of pulse voltage suggests its potential to emulate the nonlinear transmission characteristics of the synapse, and a model of multilevel conductance of the HfO_x/HfO₂ homo-bilayer structure was proposed. The device conductance continuously increases (decreases) in accordance with the number of positive (negative) voltage pulses during the potentiation (depression) process, which can emulate the change of synaptic weight in a biological synapse.

In this work, HfO_x/HfO₂ homo-bilayer structure based resistive random access memory devices were fabricated, and the resistive switching characteristics of the devices were investigated.

Multibit memory operation of metal-oxide bi-layer memristors

Emerging nanoionic memristive devices are considered as the memory technology of the future and have been winning a great deal of attention due to their ability to perform fast and at the expense of low-power and -space requirements. Their full potential is envisioned that can be fulfilled through their capacity to store multiple memory states per cell, which however has been constrained so far by issues affecting the long-term stability of independent states. Here, we introduce and evaluate a multitude of metal-oxide bi-layers and demonstrate the benefits from increased memory stability via multibit memory operation. We propose a programming methodology that allows for operating metal-oxide memristive devices as multibit memory elements with highly packed yet clearly discernible memory states. These states were found to correlate with the transport properties of the introduced barrier layers. We are demonstrating memory cells with up to 6.5 bits of information storage as well as excellent retention and power consumption performance. This paves the way for neuromorphic and non-volatile memory applications.

Compliance-Free ZrO2/ZrO2 - x /ZrO2 Resistive Memory with Controllable Interfacial Multistate Switching Behaviour

A controllable transformation from interfacial to filamentary switching mode is presented on a ZrO₂/ZrO_2 - x /ZrO₂ tri-layer resistive memory. The two switching modes are investigated with possible switching and transformation mechanisms proposed. Resistivity modulation of the ZrO_2 - x layer is proposed to be responsible for the switching in the interfacial switching mode through injecting/retracting of oxygen ions. The switching is compliance-free due to the intrinsic series resistor by the filaments formed in the ZrO₂ layers. By tuning the RESET voltages, controllable and stable multistate memory can be achieved which clearly points towards the capability of developing the next-generation multistate high-performance memory.

Digital to analog resistive switching transition induced by graphene buffer layer in strontium titanate based devices

Resistive switching behaviour can be classified into digital and analog switching based on its abrupt and gradual resistance change characteristics. Realizing the transition from digital to analog switching in the same device is essential for understanding and controlling the performance of the devices with various switching mechanisms. Here, we investigate the resistive switching in a device made with strontium titanate (SrTiO₃) nanoparticles using X-ray diffractometry, scanning electron microscopy, Raman spectroscopy, and direct electrical measurements. It is found that the well-known rupture/formation of Ag filaments is responsible for the digital switching in the device with Ag as the top electrode. To modulate the switching performance, we insert a reduced graphene oxide layer between SrTiO₃ and the bottom FTO electrode owing to its good barrier property for the diffusion of Ag ions and high out-of-plane resistance. In this case, resistive switching is changed from digital to analog as determined by the modulation of interfacial resistance under applied voltage. Based on that controllable resistance, potentiation and depression behaviours are implemented as well. This study opens up new ways for the design of multifunctional devices which are promising for memory and neuromorphic computing applications.

Four-Bits-Per-Cell Operation in an HfO2 -Based Resistive Switching Device

The quadruple-level cell technology is demonstrated in an Au/Al₂ O₃ /HfO₂ /TiN resistance switching memory device using the industry-standard incremental step pulse programming (ISPP) and error checking/correction (ECC) methods. With the highly optimistic properties of the tested device, such as self-compliance and gradual set-switching behaviors, the device shows 6σ reliability up to 16 states with a state current gap value of 400 nA for the total allowable programmed current range from 2 to 11 µA. It is demonstrated that the conventional ISPP/ECC can be applied to such resistance switching memory, which may greatly contribute to the commercialization of the device, especially competitively with NAND flash. A relatively minor improvement in the material and circuitry may enable even a five-bits-per-cell technology, which can hardly be imagined in NAND flash, whose state-of-the-art multiple-cell technology is only at three-level (eight states) to this day.

Resistive Switching Performance Improvement via Modulating Nanoscale Conductive Filament, Involving the Application of Two-Dimensional Layered Materials

Reversible chemical and structural changes induced by ionic motion and reaction in response to electrical stimuli leads to resistive switching effects in metal-insulator-metal structures. Filamentary switching based on the formation and rupture of nanoscale conductive filament has been applied in non-volatile memory and volatile selector devices with low power consumption and fast switching speeds. Before the mass production of resistive switching devices, great efforts are still required to enable stable and reliable switching performances. The conductive filament, a bridge of microscopic metal-insulator-metal structure and macroscopic resistance states, plays an irreplaceable part in resistive switching behavior, as unreliable performance often originates from unstable filament behavior. In this Review, departing from the filamentary switching mechanism and the existing issues, recent advances of the switching performance improvement through the conductive filament modulation are discussed, in the sequence of material modulation, device structure design and switching operation scheme optimization. In particular, two-dimensional (2D) nanomaterials with excellent properties including and beyond graphene, are discussed with emphasis on performance improvement by their active roles as the switching layer, insertion layer, thin electrode, patterned electrode, and edge electrode, etc.

High Performance and Enhanced Durability of Thermochromic Films Using VO2@ZnO Core-Shell Nanoparticles

For VO₂-based thermochromic smart windows, high luminous transmittance (T_lum) and solar regulation efficiency (ΔT_sol) are usually pursued as the most critical issues, which have been discussed in numerous researches. However, environmental durability, which has rarely been considered, is also so vital for practical application because it determines lifetime and cycle times of smart windows. In this paper, we report novel VO₂@ZnO core-shell nanoparticles with ultrahigh durability as well as improved thermochromic performance. The VO₂@ZnO nanoparticles-based thermochromic film exhibits a robust durability that the ΔT_sol keeps 77% (from 19.1% to 14.7%) after 10³ hours in a hyperthermal and humid environment, while a relevant property of uncoated VO₂ nanoparticles-based film badly deteriorates after 30 h. Meanwhile, compared with the uncoated VO₂-based film, the VO₂@ZnO-based film demonstrates an 11.0% increase (from 17.2% to 19.1%) in ΔT_sol and a 31.1% increase (from 38.9% to 51.0%) in T_lum. Such integrated thermochromic performance expresses good potential for practical application of VO₂-based smart windows.

Understanding rectifying and nonlinear bipolar resistive switching characteristics in Ni/SiNx/p-Si memory devices

Two resistive memory devices were prepared with different doping concentrations in the silicon bottom electrodes to explore the self-rectifying and nonlinear resistive switching characteristics of Ni/SiN_x/p-Si devices.

Manipulating resistive states in oxide based resistive memories through defective layers design

In this work, multilevel switching was achieved by a strategically designed alternative multi-layer structure with pure and Mn-doped SnO₂.

Tuning analog resistive switching and plasticity in bilayer transition metal oxide based memristive synapses

The existence of rich suboxide phases is favorable for increasing the number of weight states in transition metal oxide synapses.

Design of Electrodeposited Bilayer Structures for Reliable Resistive Switching with Self-Compliance

Programmable memory characteristics of electrodeposited CuO_x-based resistive random access memory (ReRAM) can be significantly improved by adopting a bilayer structure with a built-in current limiter. To control the on-current and enhance the device uniformity, the bilayer structure of Pt/CuO_x (switching layer)/CuO_x (current limiter)/Pt is proposed. This structure is synthesized by controlling solution pH during electrochemical deposition (ECD). The bilayer structure of Pt/CuO_x (synthesized at pH 9)/CuO_x (synthesized at pH 11.5)/Pt exhibits reliable and uniform self-compliant resistive switching behavior. The origin of resistive switching is attributed to formation and rupture of conductive filaments in the CuO_x (pH 9) layer. However, the CuO_x (pH 11.5) layer acts as the resistor without resistive switching to control the overall resistance in ReRAM. Reversible "on" and "off" switching occurs with a switching time of 100 ns. Devices based on the bilayer structure showed long data retention and good endurance. This simple use of ECD to improve the memory characteristics of electrodeposited ReRAM offers the opportunity to realize reliable and self-compliant memory devices with low-cost solution processes.

Crossbar Nanoscale HfO2-Based Electronic Synapses

Crossbar resistive switching devices down to 40 × 40 nm(2) in size comprising 3-nm-thick HfO2 layers are forming-free and exhibit up to 10(5) switching cycles. Four-nanometer-thick devices display the ability of gradual switching in both directions, thus emulating long-term potentiation/depression properties akin to biological synapses. Both forming-free and gradual switching properties are modeled in terms of oxygen vacancy generation in an ultrathin HfO2 layer. By applying the voltage pulses to the opposite electrodes of nanodevices with the shape emulating spikes in biological neurons, spike-timing-dependent plasticity functionality is demonstrated. Thus, the fabricated memristors in crossbar geometry are promising candidates for hardware implementation of hybrid CMOS-neuron/memristor-synapse neural networks.

Multistate Memristive Tantalum Oxide Devices for Ternary Arithmetic

Redox-based resistive switching random access memory (ReRAM) offers excellent properties to implement future non-volatile memory arrays. Recently, the capability of two-state ReRAMs to implement Boolean logic functionality gained wide interest. Here, we report on seven-states Tantalum Oxide Devices, which enable the realization of an intrinsic modular arithmetic using a ternary number system. Modular arithmetic, a fundamental system for operating on numbers within the limit of a modulus, is known to mathematicians since the days of Euclid and finds applications in diverse areas ranging from e-commerce to musical notations. We demonstrate that multistate devices not only reduce the storage area consumption drastically, but also enable novel in-memory operations, such as computing using high-radix number systems, which could not be implemented using two-state devices. The use of high radix number system reduces the computational complexity by reducing the number of needed digits. Thus the number of calculation operations in an addition and the number of logic devices can be reduced.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO₂-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

X-ray spectromicroscopy investigation of soft and hard breakdown in RRAM devices

Resistive random access memory (RRAM) is considered an attractive candidate for next generation memory devices due to its competitive scalability, low-power operation and high switching speed. The technology however, still faces several challenges that overall prohibit its industrial translation, such as low yields, large switching variability and ultimately hard breakdown due to long-term operation or high-voltage biasing. The latter issue is of particular interest, because it ultimately leads to device failure. In this work, we have investigated the physicochemical changes that occur within RRAM devices as a consequence of soft and hard breakdown by combining full-field transmission x-ray microscopy with soft x-ray spectroscopic analysis performed on lamella samples. The high lateral resolution of this technique (down to 25 nm) allows the investigation of localized nanometric areas underneath permanent damage of the metal top electrode. Results show that devices after hard breakdown present discontinuity in the active layer, Pt inclusions and the formation of crystalline phases such as rutile, which indicates that the temperature increased locally up to 1000 K.

Investigation of the Switching Mechanism in TiO2-Based RRAM: A Two-Dimensional EDX Approach

The next generation of nonvolatile memory storage may well be based on resistive switching in metal oxides. TiO2 as transition metal oxide has been widely used as active layer for the fabrication of a variety of multistate memory nanostructure devices. However, progress in their technological development has been inhibited by the lack of a thorough understanding of the underlying switching mechanisms. Here, we employed high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) combined with two-dimensional energy dispersive X-ray spectroscopy (2D EDX) to provide a novel, nanoscale view of the mechanisms involved. Our results suggest that the switching mechanism involves redistribution of both Ti and O ions within the active layer combined with an overall loss of oxygen that effectively render conductive filaments. Our study shows evidence of titanium movement in a 10 nm TiO2 thin-film through direct EDX mapping that provides a viable starting point for the improvement of the robustness and lifetime of TiO2-based resistive random access memory (RRAM).

Multilevel Cell Storage and Resistance Variability in Resistive Random Access Memory

Multilevel per cell (MLC) storage in resistive random access memory (ReRAM) is attractive in achieving high-density and low-cost memory and will be required in future. In this chapter, MLC storage and resistance variability and reliability of multilevel in ReRAM are discussed. Different MLC operation schemes with their physical mechanisms and a comprehensive analysis of resistance variability have been provided. Various factors that can induce variability and their effect on the resistance margin between the multiple resistance levels are assessed. The reliability characteristics and the impact on MLC storage have also been assessed.

Spatially resolved TiOx phases in switched RRAM devices using soft X-ray spectromicroscopy

Reduction in metal-oxide thin films has been suggested as the key mechanism responsible for forming conductive phases within solid-state memory devices, enabling their resistive switching capacity. The quantitative spatial identification of such conductive regions is a daunting task, particularly for metal-oxides capable of exhibiting multiple phases as in the case of TiO_x. Here, we spatially resolve and chemically characterize distinct TiO_x phases in localized regions of a TiO_x–based memristive device by combining full-field transmission X-ray microscopy with soft X-ray spectroscopic analysis that is performed on lamella samples. We particularly show that electrically pre-switched devices in low-resistive states comprise reduced disordered phases with O/Ti ratios around 1.37 that aggregate in a ~100 nm highly localized region electrically conducting the top and bottom electrodes of the devices. We have also identified crystalline rutile and orthorhombic-like TiO₂ phases in the region adjacent to the main reduced area, suggesting that the temperature increases locally up to 1000 K, validating the role of Joule heating in resistive switching. Contrary to previous studies, our approach enables to simultaneously investigate morphological and chemical changes in a quantitative manner without incurring difficulties imposed by interpretation of electron diffraction patterns acquired via conventional electron microscopy techniques.

Control of resistive switching behaviors of solution-processed HfOX-based resistive switching memory devices by n-type doping

In this study, we investigated the effect of Ni and Ta doping on resistive switching behaviors of solution-processed HfO_X-based resistive switching memory (RRAM) devices.

Associative memory realized by a reconfigurable memristive Hopfield neural network

Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

Resistance switching mode transformation in SrRuO3/Cr-doped SrZrO3/Pt frameworks via a thermally activated Ti out-diffusion process

This work reports on a mechanism for irreversible resistive switching (RS) transformation from bipolar to unipolar RS behavior in SrRuO3 (SRO)/Cr-doped SrZrO3 (SZO:Cr)/Pt capacitor structures prepared on a Ti/SiO2/Si substrate. Counter-clockwise bipolar RS memory current-voltage (I-V) characteristics are observed within the RS voltage window of -2.5 to +1.9 V, with good endurance and retention properties. As the bias voltage increases further beyond 4 V under a forward bias, a forming process occurs resulting in irreversible RS mode transformation from bipolar to unipolar mode. This switching mode transformation is a direct consequence of thermally activated Ti out-diffusion from a Ti adhesion layer. Transition metal Ti effectively out-diffuses through the loose Pt electrode layer at high substrate temperatures, leading to the unintended formation of a thin titanium oxide (TiO(x) where x < 2) layer between the Pt electrode and the SZO:Cr layer as well as additional Ti atoms in the SZO:Cr layer. Cross-sectional scanning electron microscopy, transmission electron microscopy and Auger electron spectroscopy depth-profile measurements provided apparent evidence of the Ti out-diffusion phenomenon. We propose that the out-diffusion-induced additional Ti atoms in the SZO:Cr layer contributes to the creation of the metallic filamentary channels.