Multilevel Resistive Switching Research Articles

An investigation on low operating voltage induced self-rectifying multilevel resistive switching in AgNbO3

This study presents a resistive random-access memory (RRAM) based on silver niobate (AgNbO3) perovskite films prepared via sputtering for the first time. The device exhibits a self-rectifying behavior at low operating and switching voltages, with a set voltage of about −0.4 V. Bipolar resistive switching was realized at a low operating voltage and with small set and reset voltage distributions. Compared to previously reported resistive switching perovskite materials, the device displays a superior resistive switching capability while maintaining a uniform memory window of up to 102, excellent endurance of over 104 cycles, and a long retention period of over 106 s. Moreover, the RRAM device exhibits four levels of resistive switching when the compliance current is varied, with each level of resistance state demonstrating uniformity and discrete read windows. The carrier-transport mechanism of the device was elucidated by considering various conduction mechanisms, including Schottky emission and space-charge-limited conduction. The resistive switching behavior was found to follow the conductive filamentary model, which is governed by the migration of oxygen vacancies when an electric field is applied. These outstanding properties of AgNbO3 make it a promising perovskite material option for future nonvolatile multilevel RRAM device applications.

Tuneable quantised conductance memory states in TiO2 based resistive switching devices in crossbar geometry for high density memory applications

The tunability and controllability of conductance quantization mediated multilevel resistive switching (RS) memory devices, fabricated in crossbar geometry can be a promising alternative for boosting storage density. Here, we report fabrication of Cu/TiO2/Pt based RS devices in 8 × 8 crossbar geometry, which showed reliable bipolar RS operations. The crossbar devices showed excellent spatial and temporal variability, time retention and low switching voltage (<1 V) and current (∼100 μA). Furthermore, during the reset switching, highly repeatable and reliable integral and half-integral quantized conductance (QC) was observed. The observed QC phenomenon was attributed to the two dimensional confinement of electrons as lateral width of the conducting filament (CF) matches the fermi wavelength. The magnitude and number of the QC steps were found to increase from ∼2.5 to 12.5 and from 5 to 18, respectively by increasing the compliance current (I C) from 50 to 800 μA which also increased the diameter of the CF from ∼1.2 to 3.3 nm. The enhancement in both number and magnitude of QC states was explained using electrochemical dissolution mechanism of CF of varying diameter. A thicker CF, formed at higher I C, undergoes a gradual rupture during reset process yielding a greater number of QC steps compared to a thinner CF. The realisation of QC states in the crossbar Cu/TiO2/Pt device as well as I C mediated tunability of their magnitude and number may find applications in high-density resistive memory storage devices and neuromorphic computing.

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.

One-Dimensional Covalent Organic Framework-Based Multilevel Memristors for Neuromorphic Computing.

Memristors are essential components of neuromorphic systems that mimic the synaptic plasticity observed in biological neurons. In this study, a novel approach employing one-dimensional covalent organic framework (1D COF) films was explored to enhance the performance of memristors. The unique structural and electronic properties of two 1D COF films (COF-4,4'-methylenedianiline (MDA) and COF-4,4'-oxydianiline (ODA)) offer advantages for multilevel resistive switching, which is a key feature in neuromorphic computing applications. By further introducing a TiO2 layer on the COF-ODA film, a built-in electric field between the COF-TiO2 interfaces could be generated, demonstrating the feasibility of utilizing COFs as a platform for constructing memristors with tunable resistive states. The 1D nanochannels of these COF structures contributed to the efficient modulation of electrical conductance, enabling precise control over synaptic weights in neuromorphic circuits. This study also investigated the potential of these COF-based memristors to achieve energy-efficient and high-density memory devices.

One‐Dimensional Covalent Organic Framework‐Based Multilevel Memristors for Neuromorphic Computing

AbstractMemristors are essential components of neuromorphic systems that mimic the synaptic plasticity observed in biological neurons. In this study, a novel approach employing one‐dimensional covalent organic framework (1D COF) films was explored to enhance the performance of memristors. The unique structural and electronic properties of two 1D COF films (COF‐4,4′‐methylenedianiline (MDA) and COF‐4,4′‐oxydianiline (ODA)) offer advantages for multilevel resistive switching, which is a key feature in neuromorphic computing applications. By further introducing a TiO2 layer on the COF‐ODA film, a built‐in electric field between the COF‐TiO2 interfaces could be generated, demonstrating the feasibility of utilizing COFs as a platform for constructing memristors with tunable resistive states. The 1D nanochannels of these COF structures contributed to the efficient modulation of electrical conductance, enabling precise control over synaptic weights in neuromorphic circuits. This study also investigated the potential of these COF‐based memristors to achieve energy‐efficient and high‐density memory devices.

Interface Engineering Enables Multilevel Resistive Switching in Ultra-Low-Power Chemobrionic Copper Silicate.

Memristor is assuming prominence due to its exceptionally low power consumption, adaptable, and parallel signal processing capabilities that address the limitations of the von Neumann architecture to meet the growing demand for advanced technologies such as artificial intelligence, Internet of Things (IoTs), and neuromorphic computation. In this work, we demonstrate resistive switching in copper silicate-based hollow tube-forming self-organized membrane structures belonging to the category of chemobrionics or chemical gardens to demonstrate cost-effective and highly efficient memristor devices. The device architecture is configured as ITO/PEDOT:PSS/active layer (copper silicate)/PMMA/Ag, an arrangement that serves to stabilize current-voltage hysteresis and exhibit a low SET voltage ∼0.2 V with a 0.8 nJ power consumption while manifesting robust data endurance and multilevel resistive switching. The inherent self-rectifying behavior, characterized by a high rectification ratio of 60, underscores the potential utility of these devices across a spectrum of electronic applications. To emulate the functionality of biological synapses, fundamental synaptic characteristics are assessed, including paired-pulse facilitation (PPF) and potentiation and depression (P&D). We validate the potential of copper silicate chemical garden-based memristor devices for applications that require real-time synaptic processing. Importantly, the fabrication of these devices was accomplished through a comprehensive solution-based, low-temperature process conducted under ambient environmental conditions, obviating the need for specialized glovebox facilities.

ITO/polymer/Al from diode-like to memory device: electroforming, multilevel resistive switching, and quantum point contact

ITO/polymer/Al from diode-like to memory device: electroforming, multilevel resistive switching, and quantum point contact

Silk fibroin/graphene quantum dots composite memristor with multi-level resistive switching for synaptic emulators

Silk fibroin (SF)/graphene quantum dots (GQDs) composite memristor realizes three resistance states in single scanning cycle and has the ability to simulate synaptic plasticity.

Near room temperature multilevel resistive switching memory with thin film ionic liquid crystals

Multilevel operation of nonvolatile resistive random-access memory devices was demonstrated using thin films of an ionic liquid crystal, 1-dodecyl-3-methylimidazolium tetrafluoroborate ([C12mim][BF4]), as a resistive switching layer.

Novel neuromorphic architectures based on crossbar arrays of (Co-Fe-B)&lt;sub&gt;x&lt;/sub&gt;(LiNbO&lt;sub&gt;3&lt;/sub&gt;)&lt;sub&gt;100−x&lt;/sub&gt; nanocomposite memristors

Memristor-based neuromorphic computing systems (NСSs) provide a fast, high computational and energy efficient approach to neural network (NN) training and solving cognitive problems (pattern recognition, big data processing, prediction, etc.) [1]. Memristors could be organized in large crossbar arrays to perform vector-matrix multiplication (VMM) in a natural one-step way by the weighted electrical current summation (according to the Ohm’s and Kirchhoff’s laws) [1]. In contrast, being the most massively parallel operation in NN learning and inference, VMM is extremely time- and energy-expensive in traditional von Neumann architectures. Owing to this difference, memristor-based NCSs are of high interest. Memristors have already been successfully implemented for diverse NCS realizations, and such schemes as multi-layer perceptron (MLP) [2], long short-term memory and others have been demonstrated. Most of these NCSs are usually trained by various types of gradient descent learning algorithm, the hardware realization of which is challenging due to unreliable cycle-to-cycle (c2c) and device-to-device (d2d) variations of memristive devices. Several approaches have been proposed to partially mitigate these problems, including reservoir computing [3] and fine feature engineering [4]. The general idea of such approaches is to reduce the number of required weights (i.e. memristors) compared with fully connected NNs. In this respect, such novel architectures as convolutional NN (CNN) and MLP-mixer are of high interest as they provide significant weight reduction without classification efficiency drop. Although CNN based on memristors was already demonstrated, different aspects of its realization (such as hybrid hardware-software co-design) have yet to be studied. MLP-mixer was realized only in software. Therefore, in this work we have studied the possibility of hardware realization of CNN and MLP-mixer networks based on crossbar arrays of memristors. For this purpose, we studied (Co-Fe-B)x(LiNbO3)100−x nanocomposite (CFB-LNO NC) memristors, which operate through a multifilamentary resistive switching (RS) mechanism, demonstrate high endurance, long retention and possess multilevel RS [5]. Crossbar array of memristors was fabricated using laser photolithography for patterning electrode buses and ion-beam sputtering on the original facility for active layer deposition (~10 nm thick LiNbO3 and ~290 nm thick CFB-LNO NC with x ≈10–25 at.%). Details of the fabrication process could be found elsewhere [5]. I-V curves of the fabricated memristors showed small c2c and d2d variations, plasticity with 16 different resistive states and endurance of more than 105 cycles. Using the nanocomposite based crossbar arrays, we implemented a hybrid CNN, consisting of a hardware feature extractor with one/two kernels and a software classifier. Additionally, we have demonstrated in simulation that the usage of the memristors under study in the accurately adapted MLP-Mixer architecture results in high classification accuracy that is resilient to memristive variations and stuck devices.

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor.

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2 (5 nm)/Ta2O5 (5 nm)/Pt with the Ta2O5 TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

Research on electronic synaptic simulation of HfO2-based memristor by embedding Al2O3

The potential of neuromorphic computing in synaptic simulation has led to a renewed interest in memristor. However, the demand for multilevel resistive switching with high reliability and low power consumption is still a great resistance in this application. In this work, the electronic synaptic plasticity and simulated bipolar switching behavior of Pt/Al2O3(2 nm)/HfO2 (10 nm)/Al2O3 (2 nm)/Ti tri-layer memristor is investigated. The effect of Al2O3 layer embedded at the top electrode and the bottom electrode on the resistive performance of the memristor was studied. It is found that both of them can effectively improve the reliability of the device (104 cycles), the resistive window (>103), the tunable synaptic linearity and reduce of the operating voltage. RRAM with Al2O3 embedded at the top electrode have higher uniformity and LTP linearity, while those with Al2O3 embedded at the bottom electrode significantly reduce the operating current (∼10 μA) and improve LTD linearity. Electron transport mechanisms were compared between single-layer HfO2 and tri-layer Al2O3/HfO2/Al2O3 samples under DC scanning. The results showed that the thin Al2O3 layer at the top electrode led to Fowler Northeim tunneling in the low-resistance state, while the thin Al2O3 layer at the bottom electrode led to Schottky emission in the high-resistance state. The Al2O3/HfO2/Al2O3 memristors were successfully used to achieve synaptic properties, including enhancement, inhibition, and spike time-dependent plasticity, demonstrating an important role in high-performance neuromorphic computing applications.

Multilevel resistive switching memory in lead-free double perovskite La_{2}NiFeO_{6} films

Dense and flat La_{2}NiFeO_{6} (LNFO) films were fabricated on the indium tin oxide-coated glass (ITO/glass) substrate by sol–gel method. The bipolar resistive switching behavior (BRS) could be maintained in 100 cycles and remained after 30 days, indicating that the LNFO-based RS device owned good memory stability. Surprisingly, the multilevel RS characteristics were firstly observed in the Au/LNFO/ITO/glass device. The high resistance states (HRSs) and low resistance state (LRS) with the maximum ratio of sim 500 could be remained stably in 900 s and 130 cycles, demonstrating the fine retention and endurance ability of this LNFO-based RS device. The BRS behavior of Au/LNFO/ITO/glass devices primarily obeyed the SCLC mechanism controlled by oxygen vacancies (OVs) dispersed in the LNFO layer. Under the external electric field, injected electrons were captured or discharged by OVs during trapping or detrapping process in the LNFO layer. Thus, the resistive state switched between HRS and LRS reversibly. Moreover, the modulation of Schottky-like barrier formed at the Au/LNFO interface was contributed to the resistive states switchover. It was related to the change in OVs located at the dissipative region near the Au/LNFO interface. The multilevel RS ability of LNFO-based devices in this work provides an opportunity for researching deeply on the high density RS memory in lead-free double perovskite oxides-based devices.

Lead-free CsCu2Br3 perovskite for multilevel resistive switching memory

Organic–inorganic metal halide perovskites have recently attracted enormous interest in the field of resistive switching memories owing to their unique electrical properties. Unfortunately, two challenges, the inadequate long-term stability and the toxicity of lead, largely hinder their further practical application. Herein, a 1D all-inorganic lead-free CsCu2Br3 perovskite is proposed for resistive switching devices to resolve these issues. The CsCu2Br3-based resistive switching devices exhibit typical bipolar resistive switching behavior with low set voltage, high on/off ratio (103), stable retention properties (&amp;gt;2 × 104 s), and endurance (200 cycles) in air. The unencapsulated CsCu2Br3-based device still maintains resistive switching characteristics while stored in ambient environment for over 2 months. Moreover, four on-state multilevel information storage behavior has been observed by regulating the value of compliance current during set process. The resistive switching behavior is dominated by the formation and fracture of conductive filaments, which are induced by the movement of Br− ions under electrical bias. This work offers the opportunity and strategy for the design of air-stable and environment-friendly metal halide perovskite-based memory devices.

Multilevel resistive switching memristor based on silk fibroin/graphene oxide with image reconstruction functionality

Multilevel storage memristors have great potential for use in neuromorphic computing, high-density storage, and the creation of artificial synapses. In this work, we report a multilevel storage memristor based on graphene oxide (GO)/silk fibroin (SF)/GO memristive layer structure. This memristor incorporates binary and ternary switching behaviors in a single device. It was found that the transition of the behavior of the memristor can be transformed between the two resistive switching modes by regulating the compliance current (Icc) applied to the device. Both switching behaviors are stable, repeatable, and nonvolatile. Furthermore, the device shows great potential in simulating synaptic plasticity and is applicable to use in artificial neural networks for digital image recognition as well as image compression and reconstruction. The highest accuracy of recognition of handwritten digital images based on the ternary neural network built by the device is as high as 92.3%. This work highlights GO/SF/GO memristors as promising devices for improving the storage density of memory cells and simplifying the structure of the memristor-based storage system.

Tunable Resistive Switching Behaviors and Mechanism of the W/ZnO/ITO Memory Cell.

A facile sol-gel spin coating method has been proposed for the synthesis of spin-coated ZnO nanofilms on ITO substrates. The as-prepared ZnO-nanofilm-based W/ZnO/ITO memory cell showed forming-free and tunable nonvolatile multilevel resistive switching behaviors with a high resistance ratio of about two orders of magnitude, which can be maintained for over 103 s and without evident deterioration. The tunable nonvolatile multilevel resistive switching phenomena were achieved by modulating the different set voltages of the W/ZnO/ITO memory cell. In addition, the tunable nonvolatile resistive switching behaviors of the ZnO-nanofilm-based W/ZnO/ITO memory cell can be interpreted by the partial formation and rupture of conductive nanofilaments modified by the oxygen vacancies. This work demonstrates that the ZnO-nanofilm-based W/ZnO/ITO memory cell may be a potential candidate for future high-density, nonvolatile, memory applications.

3D trigonal FAPbI3‐based multilevel resistive switching nonvolatile memory for artificial neural synapse

AbstractHybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable switch, limited reproducibility, and poor endurance are the obstacles to the practical application of the perovskite memristors. Herein, we reported a multilevel RS nonvolatile memory based on a 3D trigonal HC(NH2)2PbI3 (α‐FAPbI3) perovskite layer modified by 1‐cyanobutyl‐3‐methylimidazolium chloride ([CNBmim]Cl) and sandwiched between ITO and Au electrodes (Au/[CNBmim]Cl/α‐FAPbI3/SnO2/ITO). In contrast to the bare memristor with failure switching from low resistance state (LRS) to high resistance state (HRS), the memristor device based on the α‐FAPbI3 modified with [CNBmim]Cl (Target device) shows the retention time over 104 s with On/Off ratio (&gt;102) and endurance up to 550 cycles. The stable RS cycle benefits from the accelerated electrons de‐trapping from the reduced defects and fast charge separation in the interface of α‐FAPbI3/electrode, leading to the rupture of conductive filaments and transition of LRS to HRS. As a two‐terminal analog synaptic device, the target device can realize random handwritten digit recognition with an impressive accuracy of 89.3% on the condition of low learning phases (500 training cycles).

Poly 3-methylthiophene based memristor device for neuromorphic computing

Von Neumann computing-based memory has reached its limit, it is incapable of performing two tasks simultaneously, i.e. data storage and computation. Artificial neuromorphic computing (ANC) inspired by biological neural network is the next generation memory technology, which is based on a memristor device. Study of memristor is a hot topic in electronic research as it exhibits multifunctionality. Electrochemically synthesized doped Poly 3-methylthiophene (P3MT) based single layer memristor device showcases neuromorphic computing applications when resistance is enhanced to insulating state, by the process of de-doping. Morphological/structural deformation created by the process of de-doping on the surface of active layer is responsible for multifunctionality such as controllable negative differential resistance (NDR), unipolar resistive-switching (RS), multi-level RS (analog memory) and bio-inspired artificial synapse in SS/P3MT/Ag device geometry.

Ultralow Operating Voltage Nb2O5-Based Multilevel Resistive Memory with Direct Observation of Cu Conductive Filament

Amorphous Nb2O5 is proposed as the resistive switching layer for conductive bridge random access memory. The Nb2O5 device is fabricated with the top Cu and bottom Au electrodes, which is fabricated at a fully room temperature process, exhibiting excellent resistive switching performance with an on/off ratio of up to 105 and ultralow operating voltage. The multilevel resistive switching characteristic can be rationally demonstrated by changing the compliance current in the Nb2O5 device. Each level of the resistance state is uniform with distinguishable read windows. The reset voltage reduces significantly as the compliance current decreases, which is merely 0.002 V when the compliance current is 10–5 A. An approach of the broken-down device is elaborately developed to firmly identify the migration of Cu and the existence of a Cu conductive filament and to explain the ultralow operating voltage. Amorphous Nb2O5 devices exhibit excellent resistive switching performance, proving it is a promising material for memory applications.

Model of Multifilamentary Resistive Switching for a Memristor with Hopping Conductivity

Many applications, including learning neuromorphic computing systems, require the analogue or, at least, multilevel resistive switching (RS) of memristor devices. In metal–insulator–metal (MIM) structures it is often associated with the creation of a large number of conductive channels, or filaments, but in the majority of cases, especially for the vacancy mechanism of RS, the direct observation of many filaments is difficult or impossible. In this work, a model of the explicit filament conductivity distribution for a nanocomposite (NC) LiNbO3-based memristor, that nonetheless may be applicable to other types of MIM memristors with hopping conductivity over dangling bonds in an amorphous insulator layer, is derived from physical principles. This model, in addition to indirect evidence from magnetic and capacitance measurements, reaffirms the multifilamentary RS mechanism by the approximation of experimental data on synaptic plasticity, i.e., a change in the conductivity of a memristor by a pair of pulses applied to it. The results of this research offer opportunities to study RS in MIM structures at the macroscopic level using model filament distributions and substantiates the possibility of successfully applying the considered NC memristors as synaptic elements of neuromorphic networks.