Resistive Switching Devices Research Articles

Exploration and mitigation of protrusion behavior in Ga-ion doped h-BN memristors

Using hexagonal boron nitride (h-BN) to prepare resistive switching devices is a promising strategy. Various doping methods have aroused great interest in the semiconductor field in recent years, but many researchers have overlooked the various repetitive anomalies that occur during the testing process. In this study, the basic electrical properties and additive protrusion behavior of Ga-ion-doped h-BN memristors at micro–nanoscale during the voltage scanning process are investigated via atomic force microscopy (AFM) and energy dispersive spectroscopy. The additive protrusion behavior is subjected to exploratory research, and it is concluded that it is caused by anodic oxidation. An approach is proposed that involves filling the AFM chamber with nitrogen gas to improve the stability of memristor testing, and this method provides a solution for enhanced testing stability of memristors.

Conductance quantization in h-BN memristors

Memristive devices made of multilayer hexagonal boron nitride are attracting much attention for information storage, computation, encryption, and communication. Generating multilevel operation in these memristors would be an important added value for the field of neuromorphic computation, but the main phenomenon producing it (i.e., conductance quantization) is not well understood for this material. Here, we analyze hexagonal boron nitride memristive devices with metal electrodes operating in their quasistatic and transient regimes. Conductance quantization effects were observed both in I-V curves measured under ramped voltage stress and in the reset processes driven by voltage pulse trains. Polar and time lag plots are employed to shed light on the physics behind the device resistive switching. A model is proposed to reproduce the electrical characteristics of progressive reset processes and the discretization of the device conductance linked to quantum effects.

Assessment of the variability of the I-V characteristic of HfO2-based resistive switching devices and its simulation using the quasi-static memdiode model

Variability of the conduction characteristics of filamentary-type resistive switching devices or resistive RAMs (RRAMs) is a hot research topic both in academia and industry because it is currently considered one of the major showstoppers for the successful development and application of this technology. In this work, we thoroughly investigate the statistics of the cycle-to-cycle (C2C) variability observed in the experimental current–voltage (I-V) curves of HfO2-based memristive structures using the fitdistrplus package for the R language. This exploratory analysis allows us to identify which parametric probability distributions are the most suitable candidates for describing our data. This study involves graphical tools such as the density, skewness-kurtosis (S-K), and quantile–quantile (Q-Q) plots. The analysis is completed with the aid of goodness-of-fit statistics (Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling) and criteria (Akaike’s and Bayesian). The selected distributions are incorporated into the SPICE script of the quasi-static memdiode model for resistive switching devices and used for simulating uncorrelated C2C variability. Finally, a one-way sensitivity analysis is carried out in order to test the impact of the model parameters variation in the output characteristics of the device.

Transient form of polyvinyl alcohol-based devices with configurable resistive switching behavior for security neuromorphic computing

Physically transient resistive switching devices, a form of memory devices with the ability of achieving physical disappearance in a controllable manner, hold tremendous potentials in multiple security applications. Herein, we demonstrated a physically transient form of memristive device composed of Ag/polyvinyl alcohol/W with configurable resistive switching functionality for security neuromorphic computing. The resistive switching type of the transient device could be modulated effectively by controlling the compliance current during the set process, which was well interpreted by the filament model. Typical synaptic functions pertained to short-term plasticity (STP) and its transition from STP to long-term plasticity were vividly mimicked in this transient memristive device. Importantly, both the synaptic functions and physical form of the transient devices were capable of disappearing instantly upon immersing in de-ionized water, and the dissolution characteristics of the constituent transient materials were investigated experimentally to reveal the degradation mechanism of the device. This transient form of artificial synapse provides foreseeing perspectives on information security enhancement for neuromorphic computing systems.

Bit slicing approaches for variability aware ReRAM CIM macros

Abstract Computation-in-Memory accelerators based on resistive switching devices represent a promising approach to realize future information processing systems. These architectures promise orders of magnitudes lower energy consumption for certain tasks, while also achieving higher throughputs than other special purpose hardware such as GPUs, due to their analog computation nature. Due to device variability issues, however, a single resistive switching cell usually does not achieve the resolution required for the considered applications. To overcome this challenge, many of the proposed architectures use an approach called bit slicing, where generally multiple low-resolution components are combined to realize higher resolution blocks. In this paper, we will present an analog accelerator architecture on the circuit level, which can be used to perform Vector-Matrix-Multiplications or Matrix-Matrix-Multiplications. The architecture consists of the 1T1R crossbar array, the optimized select circuitry and an ADC. The components are designed to handle the variability of the resistive switching cells, which is verified through our verified and physical compact model. We then use this architecture to compare different bit slicing approaches and discuss their tradeoffs.

Variability and power enhancement of current controlled resistive switching devices

In this work, the unipolar resistive switching behaviour of Ni/HfO2/Si(n+) devices is studied. The structures are characterized using both current and voltage sweeps, with the device resistance and its cycle-to-cycle variability being analysed in each case. Experimental measurements indicate a clear improvement on resistance states stability when using current sweeps to induce both set and reset processes. Moreover, it has been found that using current to induce these transitions is more efficient than using voltage sweeps, as seen when analysing the device power consumption. The same results are obtained for devices with a Ni top electrode and a bilayer or pentalayer of HfO2/Al2O3 as dielectric. Finally, kinetic Monte Carlo and compact modelling simulation studies are performed to shed light on the experimental results.

Oxygen vacancy and hydrogen in amorphous HfO2

Amorphous hafnium dioxide (a-HfO2) is widely used in electronic devices, such as ultra-scaled field-effect transistors and resistive memory cells. The density of oxygen vacancy (OV) defects in a-HfO2 strongly influences the conductivity of the amorphous material. Ultimately, OV defects are responsible for the formation and rupture of conductive filament paths which are exploited in novel resistive switching devices. In this work, we studied neutral OV in a-HfO2 using methods. We investigated the formation energy of OV, the binding energy of di-OVs, the OV migration, unperturbed and in the close presence of a hydrogen atom, as well as the migration of hydrogen atom towards OV. A shallow and short-range OV migration barrier (0.6 eV) exists in a-HfO2 in contrast to the barrier (2.4 eV) in crystalline HfO2. Nearby hydrogen has a limited impact on the OV migration; however, hydrogen can diffuse easily by hopping among OVs.

Temperature Distribution in TaO x Resistive Switching Devices Assessed In Operando by Scanning Thermal Microscopy.

Understanding the physical changes during electroformation and switching processes in transition-metal-oxide-based non-volatile memory devices is important for advancing this technology. Relatively few characteristics of these devices have been assessed in operando. In this work, we present scanning thermal microscopy measurements in vacuum on TaO x -based memory devices electroformed in both positive and negative polarities and high- and low-resistance states. The observed surface temperature footprints of the filament showed higher peak temperatures and narrower temperature distributions when the top electrode served as the anode in the electroformation process. This is consistent with a model in which a hot spot is created by a gap in the conducting filament that forms closest to the anode. A similar behavior was seen on comparing the high-resistance state to the low-resistance state, with the low-resistance footprint showing a lower peak and a larger width, consistent with the gap disappearing when the device is switched from high resistance to low resistance.

Programming the Measurement System by using VEE Pro to Determine Cyclic I-V Characteristics: Resistive Switching Device Application

In this study we introduce a simple program for cyclic current–voltage (I–V) measurements for bipolar and unipolar resistive switching devices. This program (Cyclic I-V, CYC-IV) was developed under the Keysight VEE Pro (Visual Engineering Environment Program) software and has a graphical interface. CYC-IV was developed for programming the Keysight B2912 Precision Source/Measure Unit (SMU) for I-V measurement of resistive switching devices in sweep mode. CYC-IV can be used in six different sweep mode. Moreover, the ramp rate, upper and lower limits of bias, cycle delay time and number of cycles easily define by user. Measurement results were visualized in three graphs that can be viewed simultaneously with the measurements.

Electrochemical Ionic Synapses: Progress and Perspectives.

Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.

Memristor Cellular Nonlinear Networks

This paper presents a review of the theory and applications of memristor cellular nonlinear networks. By mapping the physical processes to the memristive framework, all resistive switching devices can be modeled. The idea is to find a state variable that presents with high accuracy the important features of the system, its dynamics, and time evolution in response to the inputs of the memristor. In order to develop a new design of memristor-based cellular nonlinear networks (MCNN), new circuital and mathematical memristor models need to be introduced. In this way, implementation into new software packages for computer-aided integrated circuit realization can be achieved. Another challenging problem is studying the complex behavior of MCNN models by means of local activity theory and generalizing it for various test cases. An application of the hardware implementation of these models can be found in nanostructures.

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

Resistive switching properties of SnO2 nanowires fabricated by chemical vapor deposition

Resistive switching (RS) devices have great application prospects in the emerging memory field and neuromorphic field, but their stability and unclear RS mechanism limit their relevant applications. In this work, we construct a hydrogenated Au/SnO2 nanowire (NW)/Au device with two back-to-back Schottky diodes and investigate the RS characteristics in air and vacuum. We find that the I on/I off ratio increases from 20 to 104 when the read voltage decreases from 3.1 V to −1 V under the condition of electric field. Moreover, the rectification ratio can reach as high as 104 owing to oxygen ion migration modulated by the electric field. The nanodevice also shows non-volatile resistive memory characteristic. The RS mechanism is clarified based on the changes of the Schottky barrier width and height at the interface of Au/SnO2 NW/Au device. Our results provide a strategy for designing high-performance memristive devices based on SnO2 NWs.

Device Modeling Bias in ReRAM-Based Neural Network Simulations

Emerging technologies based on resistive switching (ReRAM) devices promise to improve the speed and energy efficiency of next generation machine learning accelerators, but further research is required for achieving commercial maturity. System-level prototyping with emerging devices is costly, and algorithmic investigations require hardware neural network modeling which often deviates from experimental reality. In this work, the concept of modeling bias is proposed as a way to quantify this deviation and support reliable evaluation of device populations in the context of neural network algorithms. While applicable to other device modeling techniques, modeling bias is investigated here using jump tables - a promising physics-less technique to model emerging memory devices for hardware networks. Questions about the fidelity of these tables in relation to stochastic device behavior are answered. Two methods of jump table modeling – binning and a novel Optuna-optimized binning - are explored using synthetic data with known distributions for benchmarking and experimental data obtained from TiOx ReRAM devices for practical testing. Novel device metrics are proposed, and it is shown that these metrics can present crucial insights on the device population prior to training the hardware network. Results on a multi-layer perceptron trained on MNIST show that device models based on binning deviate from target network accuracy at a low number of points and high switching noise in the device dataset. The proposed approach opens the possibility for device-algorithm co-design investigations into statistical device models with better performance, as well as experimentally verified modeling bias in different in-memory computing and neural network architectures.

Neurotransmitter ligand effect of dopamine and cortisol conjuation on ZnO nanoparticles embedded polyvinylphenol resistive switching device

In this paper, dopamine (DA) and cortisol (CORT), two kinds of neurotransmitters, were adopted as a ligand or surface modification agent on zinc oxide (ZnO) nanoparticles (NPs) to modulate resistive switching (RS) property of the ZnO NPs embedded into polyvinylphenol (PVP) hybrid matrix. Here, digital and analog RS behaviors were demonstrated with the DA and CORT conjugations showing different ligand influence on the RS properties, respectively. For the hybrid layer, operations of bistable ON/OFF and multileveled resistance or conductance could coexist depending on the thickness and concentration of the ZnO NPs. First, conjugation of the DA on the ZnO NPs could reveal distinct memristive behaviors, which demonstrated potentiation of analog RS mode by showing gradually increased windows of the I–V hysteresis by consecutive sweeps, which were distinguished from the unconjugated ZnO NPs embedded PVP device. Secondly, in the other hand, conjugation of the CORT as the ZnO NPs’ ligand could demonstrate enhanced depression characteristics unlike the potentiation behavior induced by the DA conjugation. Therefore, since the biological neurotransmitters of DA and CORT were found to influence the RS behaviors, a novel development of bioelectronic neuromorphic device implemented for a direct interface between biological neuro-system and electronic RS device can be envisioned through this study.

The Role of Polymers in Halide Perovskite Resistive Switching Devices

Currently, halide perovskites (HPs) are gaining traction in multiple applications, such as photovoltaics and resistive switching (RS) devices. In RS devices, the high electrical conductivity, tunable bandgap, good stability, and low-cost synthesis and processing make HPs promising as active layers. Additionally, the use of polymers in improving the RS properties of lead (Pb) and Pb-free HP devices was described in several recent reports. Thus, this review explored the in-depth role of polymers in optimizing HP RS devices. In this review, the effect of polymers on the ON/OFF ratio, retention, and endurance properties was successfully investigated. The polymers were discovered to be commonly utilized as passivation layers, charge transfer enhancement, and composite materials. Hence, further HP RS improvement integrated with polymers revealed promising approaches to delivering efficient memory devices. Based on the review, detailed insights into the significance of polymers in producing high-performance RS device technology were effectively understood.

Multilevel resistive switching with negative differential resistance in Al/NiO/ZnFe2O4/ITO ReRAM device

The Resistive Random Access Memory devices have emerged as an energy-efficient alternative to Von Neumann computers by enabling in-memory computing. Here we demonstrate bipolar resistive switching in thin films of Nickel oxide (NiO) and Zinc Ferrite (ZFO) using a simple Al/NiO/ZFO/ITO configuration, making them a possible candidate for the next generation memory devices. The fabricated device demonstrated excellent resistive switching behavior with high endurance for up to 1000 cycles, good retention for more than 104 s, and a good resistance ratio of HRS to LRS ∼102. Ohmic conduction was observed in the LRS, while in the HRS, along with ohmic conduction, space charge limited current (SCLC) and Schottky mechanisms were observed. Besides the LRS and HRS, a number of stable intermediate resistance states can also be obtained during the RESET process using different stop voltages, which makes the current device a multilevel resistive switching device.

2D Ti3C2Tx MXene-derived self-assembled 3D TiO2nanoflowers for nonvolatile memory and synaptic learning applications

Two-dimensional (2D) semiconducting materials and transition-metal oxides are promising materials for nonvolatile memory and brain-inspired neuromorphic computing applications. However, it remains challenging to obtain high-quality stacked 2D films with low energy consumptions (or drive currents) because of their high interfacial resistance. In this study, we synthesized 2D Ti3C2Tx MXene-derived three-dimensional (3D) TiO2 nanoflowers (NFs) as a feasible resistive switching (RS) material with outstanding electronic properties and synaptic learning capabilities. The electrical and optical characteristics of the synthesized material were determined through density functional theory calculations. Electrical measurements of the Al/Ti3C2Tx-TiO2 NF/Pt memory device indicated the occurrence of forming-free switching phenomena with extremely low switching voltages (0.68–0.53 V), stable ON/OFF ratio (2.3 × 103), and retention greater than 105 s. The Holt–Winters exponential smoothing technique was used for modeling and predicting the switching voltages of the RS device. The mechanism underlying the reliable RS was confirmed by observing the dense conductive filaments through conductive atomic force microscopy. Interestingly, the 2D Ti3C2Tx MXene-derived 3D TiO2 NF-based RS device mimicked the potentiation/depression and spike-time-dependent plasticity of a biological synapse. Finally, a convolutional neural network was implemented based on the observed synaptic weights of Al/Ti3C2Tx-TiO2 NF/Pt for image-edge detection.

Optimization of the Cycle Numbers of TiO2 Resistive Random-Access Memory Devices by Annealing

Resistance switching (RS) devices that are based on titanium dioxide (TiO2) nanomaterials have recently attracted substantial research interest because of their simple preparation technology and potential applications in the applications of memory devices. In this paper, the RS characteristics of TiO2-based memristors were regulated by changing the crystal structure of the TiO2 film. Resistive switching memory, consisting of Ag/amorphous TiO2/Ti fabricated by anodic oxidation, exhibited poor cycling stability. Annealing was applied to the anodized sample over the temperature range of 230–680 °C, and the structure of TiO2 was investigated throughout the process. Amorphous TiO2 transformed into the anatase phase as the annealing temperature was increased to 280 °C and the stable cycle number of the device peaked at 51. By annealing at >450 °C, TiO2 was gradually transformed into the rutile phase, and the stable cycle number of the device decreased to 7. X-ray photoelectron spectroscopy confirmed that the concentration of oxygen vacancies in rutile TiO2 decreased with increasing temperature compared with that in anatase TiO2, resulting in the declining performance of the device. This work presents a low-cost annealing process to prepare stable anatase TiO2-based memristors, which will promote the industrialization of the devices for nonvolatile memory applications in the future.

From Ferroelectric Material Optimization to Neuromorphic Devices.

Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.