Filamentary Resistive Switching Research Articles

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld’s refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

Coexistence of Interfacial and Filamentary Resistance Switching in Ti/SiO x /Au Resistive Memory Devices

Coexistence of Interfacial and Filamentary Resistance Switching in Ti/SiO_<i>x</i>/Au Resistive Memory Devices

Role of Defects in the Breakdown Phenomenon of Al1-xScxN: From Ferroelectric to Filamentary Resistive Switching.

Aluminum scandium nitride (Al1-xScxN), with its large remanent polarization, is an attractive material for high-density ferroelectric random-access memories. However, the cycling endurance of Al1-xScxN ferroelectric capacitors is far below what can be achieved in other ferroelectric materials. Understanding the nature and dynamics of the breakdown mechanism is of the utmost importance for improving memory reliability. The breakdown phenomenon in ferroelectric Al1-xScxN is proposed to be an impulse thermal filamentary-driven process along preferential defective pathways. For the first time, stable and robust bipolar filamentary resistive switching in ferroelectric Al1-xScxN is reported. A hot atom damage defect generation model illustrates how filament formation and ferroelectric switching are connected. The model reveals the tendency of the ferroelectric wurtzite-type Al1-xScxN system to reach internal symmetry with bipolar electric field cycling. Defects generated from bipolar electric field cycling influence both the energy barrier between the polarization states and that required for the filament formation.

Demonstration of Synaptic Characteristics in VRRAM with TiN Nanocrystals for Neuromorphic System

AbstractTo efficiently develop an extremely intensive storage memory, the resistive random‐access memory (RRAM), which operates by producing and rupturing conductive filaments, is essential. However, due to the stochastic nature of filament production, this filamentary type resistive switching has an inherent limitation, which entails the unpredictability of the driving voltage and resistance states. Several strategies such as doping, research into multilayer stacks, and interface engineering, are suggested to tackle this challenge. This work fabricates a CMOS‐compatible TiN/HfOx/TiN‐NCs (nanocrystals)/HfOx/TiN RRAM to implement analog resistive switching and advance the development of the synaptic device. Specifically, atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) are utilized to observe the formation of TiN nanocrystals, which play a crucial role in the enhancement of resistive switching. By comparing HfOx–based RRAM devices with and without NCs, the DC I–V curves, retention, endurance, and switching speed are properly examined. Interestingly, it is found that the TiN/HfOx/TiN‐NCs/HfOx/TiN device is more appropriately utilized as an artificial synapse in neuromorphic systems mainly due to its stable and reliable resistive switching properties. Finally, this work demonstrates well‐controlled resistive switching 3D vertical RRAM with TiN‐NCs, which is particularly suitable for high‐density memory.

The g-C3N4-TiO2 nanocomposite for non-volatile memory and artificial synaptic device applications

Recently, 2D layered materials like graphitic carbon nitride (g-C3N4) are gaining significant attention due to their excellent structural and electronic properties. Metal oxide and g-C3N4 composite can enhance chemical and electronic properties which can ultimately enhance device performance. Given this, we report the synthesis of g-C3N4–TiO2 nanocomposite by the ex-situ (solid state reaction) method for resistive switching (RS) applications. Initially, the TiO2 nanoparticles (NPs) were optimized by using annealing them at different temperatures and composited with the g-C3N4. The devices fabricated using g-C3N4–TiO2 nanocomposite shows good RS properties at very low SET and RESET voltages (< 0.5 V), similar to the biological voltage scale. The distribution of switching voltages of all devices was studied using the cumulative probability and Weibull distribution techniques. Moreover, switching voltages were modeled and predicted using the statistical time series analysis method. All fabricated devices demonstrated the double-valued charge flux characteristics, suggesting the presence of a non-ideal memristor effect. The optimized g-C3N4-TiO2 (TiO2 NPs annealed at 450 °C) device shows good endurance and retention memory properties. Moreover, the optimized g-C3N4-TiO2 device can mimic the various bio-synaptic properties such as potentiation-depression, excitatory postsynaptic current, and paired-pulse facilitation. The charge transport results reveal that Ohmic and Child's square law dominated the conduction of the device. Based on electrical and charge transport results, a plausible filamentary RS effect is presented. This study suggested that the g-C3N4-TiO2 is beneficial for both memory and neuromorphic computing applications.

Collective Control of Potential-Constrained Oxygen Vacancies in Oxide Heterostructures for Gradual Resistive Switching.

Filamentary resistive switching in oxides is one of the key strategies for developing next-generation non-volatile memory devices. However, despite numerous advantages, their practical applications in neuromorphic computing are still limited due to non-uniform and indeterministic switching behavior. Given the inherent stochasticity of point defect migration, the pursuit of reliable switching likely demands an innovative approach. Herein, a collective control of oxygen vacancies is introduced in LaAlO3 /SrTiO3 (LAO/STO) heterostructures to achieve reliable and gradual resistive switching. By exploiting an electrostatic potential constraint in ultrathin LAO/STO heterostructures, the formation of conducting filaments is suppressed, but instead precisely control the concentration of oxygen vacancies. Since the conductance of the LAO/STO device is governed by the ensemble concentration of oxygen vacancies, not their individual probabilistic migrations, the resistive switching is more uniform and deterministic compared to conventional filamentary devices. It provides direct evidence for the collective control of oxygen vacancies by spectral noise analysis and modeling by Monte-Carlo simulation. As a proof of concept, the significantly-improved analog switching performance of the filament-free LAO/STO devices is demonstrated, revealing potential for neuromorphic applications. The results establish an approach to store information by point defect concentration, akin to biological ionic channels, for enhancing switching characteristics of oxide materials.

Research progress of control of physical properties of topological phase change oxide films by external field

Perovskite transition-metal oxides can undergo significant structural topological phase transition between perovskite structure, brownmillerite structure, and infinite-layer structure under the external field through the gain and loss of the oxygen ions, accompanied with significant changes in physical properties such as transportation, magnetism, and optics. Topotactic phase transformation allows structural transition without losing the crystalline symmetry of the parental phase and provides an effective platform for utilizing the redox reaction and oxygen diffusion within transition metal oxides, and establishing great potential applications in solid oxide fuel cells, oxygen sensors, catalysis, intelligent optical windows, and neuromorphic devices. In this work, we review the recent research progress of manipulating the topological phase transition of the perovskite-type oxide films and regulating their physical properties, mainly focusing on tuning the novel physical properties of these typical films through strong interaction between the lattice and electronic degrees of freedom by the action of external fields such as strain, electric field, optical field, and temperature field. For example, a giant photoinduced structure distortion in SrCoO&lt;sub&gt;2.5&lt;/sub&gt; thin film excited by photons is observed to be higher than any previously reported results in the other transition metal oxide films. The SrFeO&lt;sub&gt;2&lt;/sub&gt; films undergo an insulator-to-metal transition when the strain state changes from compressive state to tensile state. It is directly observed that perovskite SrFeO&lt;sub&gt;3&lt;/sub&gt; nanofilament is formed under the action of electric field and extends almost through the brownmillerite SrFeO&lt;sub&gt;2.5&lt;/sub&gt; matrix in the ON state and is ruptured in the OFF state, unambiguously revealing a filamentary resistance switching mechanism. Utilizing &lt;i&gt;in situ&lt;/i&gt; electrical scanning transmission electron microscopy, the transformation from brownmillerite SrFeO&lt;sub&gt;2.5&lt;/sub&gt; to infinite-layer SrFeO&lt;sub&gt;2&lt;/sub&gt; under electric field can be directly visualized with atomic resolution. We also clarify the relationship between the microscopic coupling mechanism and the macroscopic quantum properties of charges, lattices, orbits, spin, etc. Relevant research is expected to provide a platform for new materials, new approaches and new ideas for developing high-sensitivity and weak-field response electronic devices based on functional oxides. These findings about the topological phase transition in perovskite oxide films can expand the research scope of material science, and have important significance in exploring new states of matters and studying quantum critical phenomena.

Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware.

To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-to-cycle and device-to-device variations. This study experimentally demonstrated and compared the filamentary and interface-type resistive switching (RS) behaviors of tantalum oxide (Ta2O5 and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5 is a strong candidate for memristor, the filament-type RS behavior of Ta2O5 does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-type TaO2 memristor and compared it to a filament type of Ta2O5 memristor to secure the weight update characteristics and reliability. The TaO2-based interface-type memristor exhibited gradual RS characteristics and area dependency in both high- and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-term potentiation (LTP) and long-term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

Full-Inorganic Flexible Ag2S Memristor with Interface Resistance-Switching for Energy-Efficient Computing.

Flexible memristor-based neural network hardware is capable of implementing parallel computation within the memory units, thus holding great promise for fast and energy-efficient neuromorphic computing in flexible electronics. However, the current flexible memristor (FM) is mostly operated with a filamentary mechanism, which demands large energy consumption in both setting and computing. Herein, we report an Ag2S-based FM working with distinct interface resistance–switching (RS) mechanism. In direct contrast to conventional filamentary memristors, RS in this Ag2S device is facilitated by the space charge-induced Schottky barrier modification at the Ag/Ag2S interface, which can be achieved with the setting voltage below the threshold voltage required for filament formation. The memristor based on interface RS exhibits 105 endurance cycles and 104 s retention under bending condition, and multiple level conductive states with exceptional tunability and stability. Since interface RS does not require the formation of a continuous Ag filament via Ag+ ion reduction, it can achieve an ultralow switching energy of ∼0.2 fJ. Furthermore, a hardware-based image processing with a software-comparable computing accuracy is demonstrated using the flexible Ag2S memristor array. And the image processing with interface RS indeed consumes 2 orders of magnitude lower power than that with filamentary RS on the same hardware. This study demonstrates a new resistance–switching mechanism for energy-efficient flexible neural network hardware.

First-principles simulation of neutral and charged oxygen vacancies in m-ZrO2: an origin of filamentary type resistive switching

Metal oxide ZrO2 has been widely explored for resistive switching application due to excellent properties like high ON/OFF ratio, superior data retention, and low operating voltage. However, the conduction mechanism at the atomistic level is still under debate. Therefore, we have performed comprehensive insights into the role of neutral and charged oxygen vacancies in conduction filament (CF) formation and rupture, which are demonstrated using the atomistic simulation based on density functional theory (DFT). Formation energy demonstrated that the fourfold coordinated oxygen vacancy is more stable. In addition, the electronic properties of the defect included supercell confirm the improvement in electrical conductivity due to the presence of additional energy states near Fermi energy. The CF formation and rupture using threefold and fourfold oxygen vacancies are demonstrated through cohesive energy, electron localization function, and band structure. Cohesive energy analysis confirms the cohesive nature of neutral oxygen vacancies while the isolated behavior for +2 charged oxygen vacancies in the CF. In addition, nudged elastic band calculation is also performed to analyze the oxygen vacancy diffusion energy under different paths. Moreover, we have computed the diffusion coefficient and drift velocity of oxygen vacancies to understand the CF. This DFT study described detailed insight into filamentary type resistive switching observed in the experimentally fabricated device. Therefore, this fundamental study provides the platform to explore the switching mechanism of other oxide materials used for memristor device application.

Prospect and challenges of analog switching for neuromorphic hardware

To inaugurate energy-efficient hardware as a solution to complex tasks, information processing paradigms shift from von Neumann to non-von Neumann computing architectures. Emerging electronic devices compete with speed, energy, and performance to revolutionize the neural hardware system where training and inference must achieve milestones. In this Perspective, we discuss the essential criteria for training and inference in various nonvolatile neuromorphic systems such as filamentary resistive switching, interfacial resistive switching, electrochemical random-access memory, and ferroelectric memory. We present a holistic analysis of technical requirements to design ideal neuromorphic hardware in which linearity is the critical aspect during training, whereas retention is the essential criterion of inference. Finally, we evaluate the prospect of a futuristic neuromorphic hardware system by optimizing the training and inference dilemma.

Controllable resistive switching of STO:Ag/SiO2-based memristor synapse for neuromorphic computing

• STO:Ag/SiO 2 -based bilayer memristor has been fabricated for both digital and analog resistive switching. • A stable analog switching performance has been achieved in STO:Ag/SiO 2 bilayer based memristors. • Several essential synaptic functions as LTP, LTD, SRDP, PPF, and PTP have been successfully emulated. • The learning feature as spike-time dependent plasticity (STDP) has been mimicked, demonstrating the feasibility of STO:Ag/SiO 2 -based device for neuromorphic applications. Resistive random-access memory (RRAM) is a promising technology to develop nonvolatile memory and artificial synaptic devices for brain-inspired neuromorphic computing. Here, we have developed a STO:Ag/SiO 2 bilayer based memristor that has exhibited a filamentary resistive switching with stable endurance and long-term data retention ability. The memristor also exhibits a tunable resistance modulation under positive and negative pulse trains, which could fully mimic the potentiation and depression behavior like a bio-synapse. Several synaptic plasticity functions, including long-term potentiation (LTP) and long-term depression (LTD), paired-pulsed facilitation (PPF), spike-rate-dependent-plasticity (SRDP), and post-tetanic potentiation (PTP), are faithfully implemented with the fabricated memristor. Moreover, to demonstrate the feasibility of our memristor synapse for neuromorphic applications, spike-time-dependent plasticity (STDP) is also investigated. Based on conductive atomic force microscopy observations and electrical transport model analyses, it can be concluded that it is the controlled formation and rupture of Ag filaments that are responsible for the resistive switching while exhibiting a switching ratio of ~10 3 along with a good endurance and stability suitable for nonvolatile memory applications. Before fully electroforming, the gradual conductance modulation of Ag/STO:Ag/SiO 2 / p ++ -Si memristor can be realized, and the working mechanism could be explained by the succeeding growth and contraction of Ag filaments promoted by a redox reaction. This newly fabricated memristor may enable the development of nonvolatile memory and realize controllable resistance/weight modulation when applied as an artificial synapse for neuromorphic computing.

Investigation of Resistive Switching Mechanisms in Ti/TiOx/Pd‐Based RRAM Devices

AbstractResistive switching random‐access memory (RRAM) has attracted tremendous interest for applications in both embedded memory and neuromorphic computing. In this paper, two distinct types of resistive switching in Ti/TiOx/Pd‐based RRAM devices depending on the bottom electrode morphology is reported. One is filamentary resistive switching where the bottom Pd electrode has spikes caused by liftoff. The enhancement of local electric field caused by the spikes induces the formation of conductive filaments and hence leads the device to be nonvolatile. The other one is dynamic resistive switching, where the bottom electrode is flat without spikes and the device resistance changes dynamically with time. For such devices, the electric field and the oxygen ion concentration gradient in the TiOx layer have competing effects on the oxygen ion movement, which makes the Schottky barrier height at metal/oxide interface vary dynamically with time. Based on detailed physical analysis, a compact behavioral model of the dynamic RRAM device is established. The model fits well with experimentally measured device characteristics, and can be easily incorporated in deep learning algorithms. The work paves the road for further optimization of dynamic RRAM device and also its application in dynamic signal processing.

Chitosan based memory devices: filamentary versus interfacial resistive switching

Biopolymer-based memory devices have a multitude of potential applications in biomedical research. In this paper, we demonstrate the tunable memory behavior of the chitosan (CS) based memory devices as a function of CS thickness using the device structure of ITO/CS/Cu. Also, a clear distinction between filamentary and interfacial resistive switching was witnessed. High on/off ratios of 1870 and 1280 were observed in the filamentary resistive switching (for CS thickness of 80 nm) and interfacial resistive switching (for CS thickness of 200 nm) modes, respectively. The switching mechanism has also been analyzed using linear fit analysis and attributed to the formation and rupture of the conducting filaments, and charge accumulation at the metal/CS interface. This study opens up the possibility of developing write-once-read-many and read-and-write (RWM) random access memory devices.

Improved Resistive Switching of Ru: SiO2/TiO2 Based Memristive Devices

In this paper, the resistive switching behaviors of Ru:SiO2/TiO2 based memristive devices have been investigated. It is found that the random and uncontrolled formation of conductive filaments in the Ru/Ru:SiO2/p++-Si devices are crucial to realize a filamentary resistive switching. It is also found that the resistive switching behavior of Ru/Ru:SiO2/p++-Si devices could be significantly improved via inserting a TiO2 interfacial layer as in the form of Ru/Ru:SiO2/TiO2/p++-Si device structure. In the modified device, strong and stable conductive filament formation could be realized when the top electrode is positively biased. In addition to nonvolatile memory applications, an analog-type switching behavior has also been realized in our newly proposed resistive switching device. The current obtained analog conductance modulation is essential for simulating synaptic functions in electronic devices for neuromorphic applications.

Pulse frequency dependent synaptic characteristics in Ta/SiN/Si memristor device for neuromorphic system

We compare the filamentary resistive switching and the homogeneous resistive switching of the Ta/SiN/Si memristor device for the implementation of hardware-based neuromorphic system. The switching mode can be determined by the first switching curve. The low-resistance state (LRS) of filamentary resistive switching has stable retention properties without the resistance degradation, indicating the existence of different switching mechanisms between two. In homogeneous resistive switching mode, a more gradual increase and decrease in conductance control is possible. The multi-level states property provides better performance of pattern recognition when applied to neural network model. Finally, we demonstrate the improved frequency dependent conductance modulation in homogeneous resistive switching mode.

Stochastic resonance in a metal-oxide memristive device

Abstract The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type. The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driving signal is analyzed through the time series statistics of the resistive switching parameters, the spectral response to a periodic perturbation and the signal-to-noise ratio at the output of the nonlinear system. The stabilized resistive switching and the increased memristance response are revealed in the observed regularities at an optimal noise intensity corresponding to the stochastic resonance phenomenon and interpreted using a stochastic memristor model taking into account an external noise source added to the control voltage. The obtained results clearly show that noise and fluctuations can play a constructive role in nonlinear memristive systems far from equilibrium.

Noise Tailoring in Memristive Filaments.

In this study, the possibilities of noise tailoring in filamentary resistive switching memory devices are investigated. To this end, the resistance and frequency scaling of the low-frequency 1/f-type noise properties are studied in representative mainstream material systems. It is shown that the overall noise floor is tailorable by the proper material choice, as demonstrated by the order-of-magnitude smaller noise levels in Ta2O5 and Nb2O5 transition-metal oxide memristors compared to Ag-based devices. Furthermore, the variation of the resistance states allows orders-of-magnitude tuning of the relative noise level in all of these material systems. This behavior is analyzed in the framework of a point-contact noise model highlighting the possibility for the disorder-induced suppression of the noise contribution arising from remote fluctuators. These findings promote the design of multipurpose resistive switching units, which can simultaneously serve as analog-tunable memory elements and tunable noise sources in probabilistic computing machines.

Ti3C2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications.

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti3C2 MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti3C2 MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti3C2 MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti3C2/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti3C2/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti3C2 MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti3C2 MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.

Origin of resistance state relaxation and nonvolatile features in NiO films: Interfacial vs filamentary resistive switching

NiO films grown on Pt and Nb:SrTiO3 (NSTO) substrates display filament-type and interface-type resistance switching (RS), respectively. The resistance retention studies show that these two different RSs also lead to distinct resistance evolutions. For Pt/NiO/Pt, both high and low resistance states have good retention property, demonstrating that the conducting filament is stable once it is formed. However, for Pt/NiO/NSTO/In, all resistance states show time-relaxation, and the relaxation trend depends on the polarity of the write bias. The resistive relaxation and nonvolatile features are attributed to the charge migration and electron trapping scenario, respectively.