Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Reconfigurable Inverter Based on Ferroelectric-Gating MoS2 Field-Effect Transistors toward In-Memory Logic Operations

With the advancement of information technology in contemporary society, there is an increasing demand for the rapid processing of large amounts of data. Concurrently, traditional silicon-based integrated circuits have reached their performance limits due to the exacerbation of non-ideal effects. This necessitates further multifunctionalities and miniaturization of modern integrated circuits. In recent years, two-dimensional (2D) materials have demonstrated exceptional physical and electrical properties and have emerged as a promising method for the development of next-generation electronic devices. Ferroelectric materials enable the flexible adjustment of polarization states, thereby simultaneously achieving non-volatile memory and the modulation of carrier transport. Moreover, reconfigurable logic allows for the dynamic adjustment of computational functions when different tasks are executed, significantly enhancing logical operation capabilities. Here, we report a reconfigurable logic inverter based on ferroelectric-gating MoS2 field-effect transistors. Notably, the ferroelectric transistor achieves a high Ion/Ioff ratio of ∼106 and a memory window of ∼20 V. Furthermore, the reconfigurable inverter realized using two as-fabricated ferroelectric field-effect transistors (FeFETs) can produce three distinct output logics (including always "0", always "1", and inverter) in different polarization states under the same input. This study provides a strategy for achieving reconfigurable logic in ferroelectric-gating transistors, thereby offering a potential functional block for the development of in-memory computing.

Analysis and fully memristor-based reservoir computing for temporal data classification

Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared to conventional recurrent neural networks. A key component in its hardware deployment is the ability to generate dynamic reservoir states. Our research introduces a novel dual-memory RC system, integrating a short-term memory via a WOx-based memristor, capable of achieving 16 distinct states encoded over 4 bits, and a long-term memory component using a TiOx-based memristor within the readout layer. We thoroughly examine both memristor types and leverage the RC system to process temporal data sets. The performance of the proposed RC system is validated through two benchmark tasks: isolated spoken digit recognition and with only a fraction of complete samples forecasting the Mackey-Glass (MG) time series prediction. The system delivered an impressive 98.84% accuracy in speech digit recognition and sustained a low normalized root mean square error (NRMSE) of 0.036 in the time series prediction task, underscoring its capability. This study illuminates the adeptness of memristor-based RC systems in managing intricate temporal challenges, laying the groundwork for further innovations in neuromorphic computing.

Localized Conduction Channels in Memristors

Since the early 2000s, the impending end of Moore's scaling, as the physical limits to shrinking transistors have been approached, has fueled interest in improving the functionality and efficiency of integrated circuits by employing memristors or two-terminal resistive switches. Formation (or avoidance) of localized conducting channels in many memristors, often called "filaments", has been established as the basis for their operation. While we understand some qualitative aspects of the physical and thermodynamic origins of conduction localization, there are not yet quantitative models that allow us to predict when they will form or how large they will be. Here we compile observations and explanations of channel formation that have appeared in the literature since the 1930s, show how many of these seemingly unrelated pieces fit together, and outline what is needed to complete the puzzle. This understanding will be a necessary predictive component for the design and fabrication of post-Moore's-era electronics.

Advances in Metal Halide Perovskite Memristors: A Review from a Co-Design Perspective

The memristor has recently demonstrated considerable potential in the field of large-scale data information processing. Metal halide perovskites (MHPs) have emerged as the leading contenders for memristors due to their sensitive optoelectronic response, low power consumption, and ability to be prepared at low temperatures. This work presents a comprehensive enumeration and analysis of the predominant research advancements in mechanisms of resistance switch (RS) behaviors in MHPs-based memristors, along with a summary of useful characterization techniques. The impact of diverse optimization techniques on the functionality of perovskite memristors is examined and synthesized. Additionally, the potential of MHPs memristors in data processing, physical encryption devices, artificial synapses, and brain-like computing advancement of MHPs memristors is evaluated. This review can prove a valuable reference point for the future development of perovskite memristors applications. In conclusion, the current challenges and prospects of MHPs-based memristors are discussed in order to provide insights into potential avenues for the development of next-generation information storage technologies and biomimetic applications.

Cryo-SIMPLY: A Reliable STT-MRAM-Based Smart Material Implication Architecture for In-Memory Computing

This paper presents Cryo-SIMPLY, a reliable smart material implication (SIMPLY) operating at cryogenic conditions (77 K). The assessment considers SIMPLY schemes based on spin-transfer torque magnetic random access memory (STT-MRAM) technology with single-barrier magnetic tunnel junction (SMTJ) and double-barrier magnetic tunnel junction (DMTJ). Our study relies on a temperature-aware macrospin-based Verilog-A compact model for MTJ devices and a 65 nm commercial process design kit (PDK) calibrated down to 77 K under silicon measurements. The DMTJ-based SIMPLY demonstrates a significant improvement in read margin at 77 K, overcoming the conventional SIMPLY scheme at room temperature (300 K) by approximately 2.3 X. When implementing logic operations with the SIMPLY scheme operating at 77 K, the DMTJ-based scheme assures energy savings of about 69%, as compared to its SMTJ-based counterpart operating at 77 K. Overall, our results prove that the SIMPLY scheme at cryogenic conditions is a promising solution for reliable and energy-efficient logic-in-memory (LIM) architectures.

Density Functional Theory Insights into Conduction Mechanisms in Perovskite-Type RCoO3 Nanofibers for Future Resistive Random-Access Memory Applications

In the era of artificial intelligence and Internet of Things, data storage has an important impact on the future development direction of data analysis. Resistive random-access memory (RRAM) devices are the research hotspot in the era of artificial intelligence and Internet of Things. Perovskite-type rare-earth metal oxides are common functional materials and considered promising candidates for RRAM devices because their interesting electronic properties depend on the interaction between oxygen ions, transition metals, and rare-earth metals. LaCoO3, NdCoO3, and SmCoO3 are typical rare-earth cobaltates (RCoO3). These perovskite materials were fabricated by electrospinning and the calcination method. The aim of this study was to investigate the resistive switching effect in the RCoO3 structure. The oxygen vacancies in RCoO3 are helpful to form conductive filaments, which dominates the resistance transition mechanism of Pt/RCoO3/Pt. The electronic properties of RCoO3 were investigated, including the barrier height and the shape of the conductive filaments. This study confirmed the potential application of LaCoO3, NdCoO3, and SmCoO3 in memory storage devices.

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Resistive Switching Characteristics of NiO Thin Films Influenced by Changes in the Diameter of Nanometer-Scale Top Electrodes

We investigated the forming, set, and reset voltages affected by the area of the top electrodes of a resistive random access memory (RRAM) capacitor fabricated by epitaxial NiO thin films. NiO RRAM capacitors with Au top electrode with a diameter of 100 μm showed typical unipolar switching characteristics. Au top electrodes with diameters of 10, 20, and 30 nm were formed on the surface of epitaxial NiO thin films by e-beam lithography. The forming, set, and reset voltages tended to decrease as the diameter of NiO RRAM capacitors with nanosized Au top electrodes decreased. As the area of the Au top electrodes increases, the volume of space in which the conductive filaments formed within the NiO RRAM capacitors can be formed becomes larger. This causes the conductive filament to be formed at relatively low energy, giving low forming voltages, while increasing the diversity of paths, causing a wide forming voltage distribution.

Quantum Dots for Resistive Switching Memory and Artificial Synapse

Memristor devices for resistive-switching memory and artificial synapses have emerged as promising solutions for overcoming the technological challenges associated with the von Neumann bottleneck. Recently, due to their unique optoelectronic properties, solution processability, fast switching speeds, and low operating voltages, quantum dots (QDs) have drawn substantial research attention as candidate materials for memristors and artificial synapses. This review covers recent advancements in QD-based resistive random-access memory (RRAM) for resistive memory devices and artificial synapses. Following a brief introduction to QDs, the fundamental principles of the switching mechanism in RRAM are introduced. Then, the RRAM materials, synthesis techniques, and device performance are summarized for a relative comparison of RRAM materials. Finally, we introduce QD-based RRAM and discuss the challenges associated with its implementation in memristors and artificial synapses.

Memristive Architectures Exploiting Self-Compliance Multilevel Implementation on 1 kb Crossbar Arrays for Online and Offline Learning Neuromorphic Applications

This paper suggests the practical implications of utilizing a high-density crossbar array with self-compliance (SC) at the conductive filament (CF) formation stage. By limiting the excessive growth of CF, SC functions enable the operation of a crossbar array without access transistors. An AlOx/TiOy, internal overshoot limitation structure, allows the SC to have resistive random-access memory. In addition, an overshoot-limited memristor crossbar array makes it possible to implement vector-matrix multiplication (VMM) capability in neuromorphic systems. Furthermore, AlOx/TiOy structure optimization was conducted to reduce overshoot and operation current, verifying uniform bipolar resistive switching behavior and analog switching properties. Additionally, extensive electric pulse stimuli are confirmed, evaluating long-term potentiation (LTP), long-term depression (LTD), and other forms of synaptic plasticity. We found that LTP and LTD characteristics for training an online learning neural network enable MNIST classification accuracies of 92.36%. The SC mode quantized multilevel in offline learning neural networks achieved 95.87%. Finally, the 32 × 32 crossbar array demonstrated spiking neural network-based VMM operations to classify the MNIST image. Consequently, weight programming errors make only a 1.2% point of accuracy drop to software-based neural networks.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Research on Residual Gas Adsorption on Surface of Hexagonal Boron Nitride-Based Memristor

As a promising nonvolatile memory device with two ends, the memristor has received extensive attention for its industrial manufacture. Density functional theory was used to analyze the adsorption properties of residual gas on hexagonal boron nitride (h-BN)-based memristor model surfaces with Stone-Wales-5577 grain boundary defects [h-BN(SW)]. First, by calculating the adsorption energy, geometric parameters, and charge transfer, we identified the most stable adsorption sites for hydrogen atoms (H-TB1) and H2 molecules (H2-TN2). We observed a tendency toward chemisorption for hydrogen atoms and physical adsorption for H2 molecules at these sites. Furthermore, two coadsorption configurations were formed by introducing H2 molecules and hydrogen atoms into single adsorption configurations: namely H-TB1_H2-TN1TN2 and H2-TN2_H-TB1TN1TN3. In the case of hydrogen-based configuration, there is weak dissociation of the H2 molecule, which does not facilitate hydrogen atom adsorption. However, adjacent hydrogen atoms tend to form stable dimers, while excess hydrogen atoms have a tendency to weakly chemisorb in the case of H2-based configuration. The pristine h-BN surface is more favorable for hydrogen atom migration compared to the h-BN(SW) surface due to its higher adsorption energy. On the h-BN(SW) surface, hydrogen atoms tend to migrate inward from the center of adjacent heptagonal boron nitride rings while coadsorption has a minimal impact on their vertical migration as well as that of H2 molecules. This work provides theoretical insights into the H/H2 trace gas interaction during h-BN wafer-level fabrication for memristor devices.

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

Optimization of Bilayer Resistive Random Access Memory Based on Ti/HfO2/ZrO2/Pt

In this paper, the electrothermal coupling model of metal oxide resistive random access memory (RRAM) is analyzed by using a 2D axisymmetrical structure in COMSOL Multiphysics simulation software. The RRAM structure is a Ti/HfO2/ZrO2/Pt bilayer structure, and the SET and RESET processes of Ti/HfO2/ZrO2/Pt are verified and analyzed. It is found that the width and thickness of CF1 (the conductive filament of the HfO2 layer), CF2 (the conductive filament of the ZrO2 layer), and resistive dielectric layers affect the electrical performance of the device. Under the condition of the width ratio of conductive filament to transition layer (6:14) and the thickness ratio of HfO2 to ZrO2 (7.5:7.5), Ti/HfO2/ZrO2/Pt has stable high and low resistance states. On this basis, the comparison of three commonly used RRAM metal top electrode materials (Ti, Pt, and Al) shows that the resistance switching ratio of the Ti electrode is the highest at about 11.67. Finally, combining the optimal conductive filament size and the optimal top electrode material, the I-V hysteresis loop was obtained, and the switching ratio Roff/Ron = 10.46 was calculated. Therefore, in this paper, a perfect RRAM model is established, the resistance mechanism is explained and analyzed, and the optimal geometrical size and electrode material for the hysteresis characteristics of the Ti/HfO2/ZrO2/Pt structure are found.

Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems

Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.

High-density via RRAM cell with multi-level setting by current compliance circuits

In this work, multi-level storage in the via RRAM has been first time reported and demonstrated with the standard FinFET CMOS logic process. Multi-level states in via RRAM are achieved by controlling the current compliance during set operations. The new current compliance setting circuits are proposed to ensure stable resistance control when one considers cells under the process variation effect. The improved stability and tightened distributions on its multi-level states on via RRAM have been successfully demonstrated.

Effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices: the role of ZnO grain boundaries

The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 °C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 °C to 700 °C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 °C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution σ/μ, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.

Quantum Conductance in Vertical Hexagonal Boron Nitride Memristors with Graphene-Edge Contacts

Two-dimensional materials (2DMs) have gained significant interest for resistive-switching memory toward neuromorphic and in-memory computing (IMC). To achieve atomic-level miniaturization, we introduce vertical hexagonal boron nitride (h-BN) memristors with graphene edge contacts. In addition to enabling three-dimensional (3D) integration (i.e., vertical stacking) for ultimate scalability, the proposed structure delivers ultralow power by isolating single conductive nanofilaments (CNFs) in ultrasmall active areas with negligible leakage thanks to atomically thin (∼0.3 nm) graphene edge contacts. Moreover, it facilitates studying fundamental resistive-switching behavior of single CNFs in CVD-grown 2DMs that was previously unattainable with planar devices. This way, we studied their programming characteristics and observed a consistent single quantum step in conductance attributed to unique atomically constrained nanofilament behavior in CVD-grown 2DMs. This resistive-switching property was previously suggested for h-BN memristors and linked to potential improvements in stability (robustness of CNFs), and now we show experimental evidence including superior retention of quantized conductance.

Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition

Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (βLTP/βLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).

An Artificial Olfactory System Based on a Memristor Can Simulate Organ Injury and Functions in Air Purification

Artificial olfactory systems are receiving increasing attention because of their potential applications in humanoid robots, artificial noses, and the next generation of human-computer interactions. However, simulating the human olfactory system, which recognizes, remembers, and automatically takes protective measures against gases, remains a challenge. In this paper, a WO3-TiO2@Ag NPs (silver nanoparticle) gas sensor was prepared by the sol-gel method, and an Al/pectin:AgNP/ITO memristor was prepared by spin coating and vacuum evaporation. The gas sensor has been combined with the memristor to simulate physical damage to humans in a dangerous gas environment for a long time, and an artificial olfactory system is constructed by field-programmable gate array external control. The WO3-TiO2@Ag NPs gas sensor can sense and identify ethanol vapor through changes in resistance, and the signal transmitted to the pectin-based memristor can switch the resistance state of the memristor to store gas information. Furthermore, the activation of the memristor can also trigger rotation of the fan to purify the gas and reduce damage caused by excessive exposure to dangerous gases. This artificial olfactory system provides a promising strategy for the development of artificial intelligence and human-computer interaction systems.

Wide-Range Synaptic Current Responses with a Liquid Ga Electrode via a Surface Redox Reaction in a NaOH Solution at Different Molar Concentrations

A liquid Ga-based synaptic device with two-terminal electrodes is demonstrated in NaOH solutions at 50 °C. The proposed electrochemical redox device using the liquid Ga electrode in the NaOH solution can emulate various biological synapses that require different decay constants. The device exhibits a wide range of current decay times from 60 to 320 ms at different NaOH mole concentrations from 0.2 to 1.6 M. This research marks a step forward in the development of flexible and biocompatible neuromorphic devices that can be utilized for a range of applications where different synaptic strengths are required lasting from a few milliseconds to seconds.

Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing

The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I-V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.

Effect of electrode materials on resistive switching behaviour of NbOx-based memristive devices

Memristive devices that rely on redox-based resistive switching mechanism have attracted great attention for the development of next-generation memory and computing architectures. However, a detailed understanding of the relationship between involved materials, interfaces, and device functionalities still represents a challenge. In this work, we analyse the effect of electrode metals on resistive switching functionalities of NbOx-based memristive cells. For this purpose, the effect of Au, Pt, Ir, TiN, and Nb top electrodes was investigated in devices based on amorphous NbOx grown by anodic oxidation on a Nb substrate exploited also as counter electrode. It is shown that the choice of the metal electrode regulates electronic transport properties of metal-insulator interfaces, strongly influences the electroforming process, and the following resistive switching characteristics. Results show that the electronic blocking character of Schottky interfaces provided by Au and Pt metal electrodes results in better resistive switching performances. It is shown that Pt represents the best choice for the realization of memristive cells when the NbOx thickness is reduced, making possible the realization of memristive cells characterised by low variability in operating voltages, resistance states and with low device-to-device variability. These results can provide new insights towards a rational design of redox-based memristive cells.

Spatially-Resolved Thermometry of Filamentary Nanoscale Hot Spots in TiO2 Resistive Random Access Memories to Address Device Variability

Resistive random access memories (RRAM), based on the formation and rupture of conductive nanoscale filaments, have attracted increased attention for application in neuromorphic and in-memory computing. However, this technology is, in part, limited by its variability, which originates from the stochastic formation and extreme heating of its nanoscale filaments. In this study, we used scanning thermal microscopy (SThM) to assess the effect of filament-induced heat spreading on the surface of metal oxide RRAMs with different device designs. We evaluate the variability of TiO2 RRAM devices with area sizes of 2 × 2 and 5 × 5 μm2. Electrical characterization shows that the variability indicated by the standard deviation of the forming voltage is ∼2 times larger for 5 × 5 μm2 devices than for the 2 × 2 μm2 ones. Further knowledge on the reason for this variability is gained through the SThM thermal maps. These maps show that for 2 × 2 μm2 devices the formation of one filament, i.e., hot spot at the device surface, happens reliably at the same location, while the filament location varies for the 5 × 5 μm2 devices. The thermal information, combined with the electrical, interfacial, and geometric characteristics of the device, provides additional insights into the operation and variability of RRAMs. This work suggests thermal engineering and characterization routes to optimize the efficiency and reliability of these devices.

Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

The analog resistive switching properties of amorphous InGaZnOx (a-IGZO)-based devices with Al as the top and bottom electrodes and an Al-Ox interface layer inserted on the bottom electrode are presented here. The influence of the electrode deposition rate on the surface roughness was established and proposed as the cause of the observed unusual anomalous switching effects. The DC electrical characterization of the optimized Al/a-IGZO/AlOx/Al devices revealed an analog resistive switching with a satisfactory value for retention levels, but the endurance was found to decrease after 200 cycles. The predominant conduction mechanism in these devices was found to be thermionic emission. An in-depth analysis was performed to explore the relaxation kinetics of the device and it was found that the current has a lower decay rate. The current level stability was tested and found reliable even after 5 h. The cost-effective and precious metal-free nature of the a-IGZO memristor investigated in this study makes it a highly desirable candidate for neuromorphic computing applications.

Improved Uniformity of TaOx-Based Resistive Switching Memory Device by Inserting Thin SiO2 Layer for Neuromorphic System

RRAM devices operating based on the creation of conductive filaments via the migration of oxygen vacancies are widely studied as promising candidates for next-generation memory devices due to their superior memory characteristics. However, the issues of variation in the resistance state and operating voltage remain key issues that must be addressed. In this study, we propose a TaOx/SiO2 bilayer device, where the inserted SiO2 layer localizes the conductive path, improving uniformity during cycle-to-cycle endurance and retention. Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) confirm the device structure and chemical properties. In addition, various electric pulses are used to investigate the neuromorphic system properties of the device, revealing its good potential for future memory device applications.

Semiempirical Two-Dimensional Model of the Bipolar Resistive Switching Process in Si-NCs/SiO2 Multilayers

In this work, the SET and RESET processes of bipolar resistive switching memories with silicon nanocrystals (Si-NCs) embedded in an oxide matrix is simulated by a stochastic model. This model is based on the estimation of two-dimensional oxygen vacancy configurations and their relationship with the resistive state. The simulation data are compared with the experimental current-voltage data of Si-NCs/SiO2 multilayer-based memristor devices. Devices with 1 and 3 Si-NCs/SiO2 bilayers were analyzed. The Si-NCs are assumed as agglomerates of fixed oxygen vacancies, which promote the formation of conductive filaments (CFs) through the multilayer according to the simulations. In fact, an intermediate resistive state was observed in the forming process (experimental and simulated) of the 3-BL device, which is explained by the preferential generation of oxygen vacancies in the sites that form the complete CFs, through Si-NCs.

Synaptic plasticity and learning behaviour in multilevel memristive devices

This research explores a novel two-terminal heterostructure of the Pt/Cu2Se/Sb2Se3/FTO memristor, which exhibited essential biological synaptic functions. These synaptic functions play a critical role in emulating biological neural systems and overcoming the limitations of traditional computing architectures. By repeating a fixed pulse train, in this study, we realized a few crucial neural functions toward weight modulation, such as nonlinear conductance changes and potentiation/depression characteristics, which aid the transition of short-term memory to long-term memory. However, we also employed multilevel switching, which provides easily accessible multilevel (4-states, 2-bit) states, for high-density data storage capability along with endurance (102 pulse cycles for each state) in our proposed device. In terms of synaptic plasticity, the device performed well by controlling the pulse voltage and pulse width during excitatory post-synaptic current (EPSC) measurements. The spike-time-dependent plasticity (STDP) highlights their outstanding functional properties, indicating that the device can be used in artificial biological synapse applications. The artificial neural network with Pt/Cu2Se/Sb2Se3/FTO achieved a significant accuracy of 73% in the simulated Modified National Institute of Standards and Technology database (MNIST) pattern. The conduction mechanism of resistive switching and the artificial synaptic phenomena could be attributed to the transfer of Se2- ions and selenium vacancies. The neuromorphic characteristics of the Pt/Cu2Se/Sb2Se3/FTO devices demonstrate their potential as futuristic synaptic devices.

Degradable Carrageenan as a Substrate and Resistive Material for Flexible Applications

In recent years, due to the environmental impact caused by electronic waste, decomposable components have become one of the most important topics in the world. In this study, the carrageenan material extracted from red algae was used as the resistance-switching layer of electronic components, and potassium was added to the carrageenan as a substrate (CK). CK has the advantages of excellent mechanical properties, transparency, and decomposability. In addition, the In/carrageenan/Ag/CK (ICACK) device exhibits good memory properties with a high ON/OFF ratio exceeding 10⁷ and a retention time exceeding 10⁴ s. Due to the doping of potassium ions, the ICACK element has a fairly good bending performance. Although bending or stretching under a small radius of curvature will not have a great impact on the electrical performance, it shows that in the future wearable or good potential in the field of implantable devices.

Selective Dual-Ion Modulation in Solid-State Magnetoelectric Heterojunctions for In-Memory Encryption

Nanoionic technologies are identified as a promising approach to modulating the physical properties of solid-state dielectrics, which have resulted in various emergent nanodevices, such as nanoionic resistive switching devices and magnetoionic devices for memory and computing applications. Previous studies are limited to single-type ion manipulation, and the investigation of multiple-type ion modulation on the coupled magnetoelectric effects, for developing information devices with multiple integrated functionalities, remains elusive. Here, a dual-ion solid-state magnetoelectric heterojunction based on Pt/HfO_{2- x} /NiO_y /Ni with reconfigurable magnetoresistance (MR) characteristics is reported for in-memory encryption. It is shown that the oxygen anions and nickel cations can be selectively driven by voltages with controlled polarity and intensity, which concurrently change the overall electrical resistance and the interfacial magnetic coupling, thus significantly modulate the MR symmetry. Based on this device, a magnetoelectric memory prototype array with in-memory encryption functionality is designed for the secure storage of image and digit information. Along with the advantages including simple structure, multistate encryption, good reversibility, and nonvolatile modulation capability, this proof-of-concept device opens new avenues toward next-generation compact electronics with integrated information functionalities.

Effects of Top and Bottom Electrodes Materials and Operating Ambiance on the Characteristics of MgFx Based Bipolar RRAMs

The effects of electrode materials (top and bottom) and the operating ambiances (open-air and vacuum) on the MgF_x-based resistive random-access memory (RRAM) devices are studied. Experiment results show that the device's performance and stability depend on the difference between the top and bottom electrodes' work functions. Devices are robust in both environments if the work function difference between the bottom and top electrodes is greater than or equal to 0.70 eV. The operating environment-independent device performance depends on the surface roughness of the bottom electrode materials. Reducing the bottom electrodes' surface roughness will reduce moisture absorption, minimizing the impact of the operating environment. Ti/MgF_x/p⁺-Si memory devices with the minimum surface roughness of the p⁺-Si bottom electrode show operating environment-independent electroforming-free stable resistive switching properties. The stable memory devices show promising data retentions of >10⁴ s in both environments with DC endurance properties of more than 100 cycles.

Electrochemical Resistive Switching in Nanoporous Hybrid Films by One-Step Molecular Layer Deposition

An organic-inorganic hybrid resistive random-access memory based on a nanoporous zinc-based hydroquinone (Zn-HQ) thin film has been constructed with a Pt/Zn-HQ/Ag sandwich structure. The porous Zn-HQ functional layer was directly fabricated by a one-step molecular layer deposition. These Pt/Zn-HQ/Ag devices show a typical electroforming-free bipolar resistive switching characteristic with lower operation voltages and higher on/off ratio above 10². Our nanoporous hybrid devices can also realize multilevel storage capability and exhibit excellent endurance/retention properties. The connection and disconnection of Ag conductive filaments in nanoporous Zn-HQ thin film follow the electrochemical metallization mechanism. Our computational simulations confirm that the existence of nanopores in Zn-HQ thin films facilitates the Ag filament formation, contributing to the high performance of our hybrid devices.

Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices

A three-terminal synaptic transistor enables more accurate controllability over the conductance compared with traditional two-terminal synaptic devices for the synaptic devices in hardware-oriented neuromorphic systems. In this work, we fabricated IGZO-based three-terminal devices comprising HfAlOx and CeOx layers to demonstrate the synaptic operations. The chemical compositions and thicknesses of the devices were verified by transmission electron microscopy and energy dispersive spectroscopy in cooperation. The excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), and short-term depression (STD) of the synaptic devices were realized for the short-term memory behaviors. The IGZO-based three-terminal synaptic transistor could thus be controlled appropriately by the amplitude, width, and interval time of the pulses for implementing the neuromorphic systems.

A Unified Current-Voltage Model for Metal Oxide-Based Resistive Random-Access Memory

Resistive random-access memory (RRAM) is essential for developing neuromorphic devices, and it is still a competitive candidate for future memory devices. In this paper, a unified model is proposed to describe the entire electrical characteristics of RRAM devices, which exhibit two different resistive switching phenomena. To enhance the performance of the model by reflecting the physical properties such as the length index of the undoped area during the switching operation, the Voltage ThrEshold Adaptive Memristor (VTEAM) model and the tungsten-based model are combined to represent two different resistive switching phenomena. The accuracy of the I-V relationship curve tails of the device is improved significantly by adjusting the ranges of unified internal state variables. Furthermore, the unified model describes a variety of electrical characteristics and yields continuous results by using the device's current-voltage relationship without dividing its fitting conditions. The unified model describes the optimized electrical characteristics that reflect the electrical behavior of the device.

Enhanced Short-Term Memory Plasticity of WOx-Based Memristors by Inserting AlOx Thin Layer

ITO/WO_x/TaN and ITO/WO_x/AlO_x/TaN memory cells were fabricated as a neuromorphic device that is compatible with CMOS. They are suitable for the information age, which requires a large amount of data as next-generation memory. The device with a thin AlO_x layer deposited by atomic layer deposition (ALD) has different electrical characteristics from the device without an AlO_x layer. The low current is achieved by inserting an ultra-thin AlO_x layer between the switching layer and the bottom electrode due to the tunneling barrier effect. Moreover, the short-term memory characteristics in bilayer devices are enhanced. The WO_x/AlO_x device returns to the HRS without a separate reset process or energy consumption. The amount of gradual current reduction could be controlled by interval time. In addition, it is possible to maintain LRS for a longer time by forming it to implement long-term memory.

Non-Volatile Memory and Synaptic Characteristics of TiN/CeOx/Pt RRAM Devices

In this study, we investigate the synaptic characteristics and the non-volatile memory characteristics of TiN/CeOx/Pt RRAM devices for a neuromorphic system. The thickness and chemical properties of the CeOx are confirmed through TEM, EDS, and XPS analysis. A lot of oxygen vacancies (ions) in CeOx film enhance resistive switching. The stable bipolar resistive switching characteristics, endurance cycling (>100 cycles), and non-volatile properties in the retention test (>10,000 s) are assessed through DC sweep. The filamentary switching model and Schottky emission-based conduction model are presented for TiN/CeOx/Pt RRAM devices in the LRS and HRS. The compliance current (1~5 mA) and reset stop voltage (−1.3~−2.2 V) are used in the set and reset processes, respectively, to implement multi-level cell (MLC) in DC sweep mode. Based on neural activity, a neuromorphic system is performed by electrical stimulation. Accordingly, the pulse responses achieve longer endurance cycling (>10,000 cycles), MLC (potentiation and depression), spike-timing dependent plasticity (STDP), and excitatory postsynaptic current (EPSC) to mimic synapse using TiN/CeOx/Pt RRAM devices.

Effects of Oxygen Flow Rate on Metal-to-Insulator Transition Characteristics in NbOx-Based Selectors

In this work, NbOx-based selector devices were fabricated by sputtering deposition systems. Metal-to-insulator transition characteristics of the device samples were investigated depending on the oxygen flow rate (3.5, 4.5, and 5.5 sccm) and the deposition time. The device stack was scanned by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). The yields, including MIT, nonlinear, and Ohmic, in working devices with different deposition conditions were also evaluated. Moreover, we observed the trend in yield values as a function of selectivity. In addition, the current-voltage (I-V) curves were characterized in terms of DC and pulse endurance. Finally, the switching speed and operating energies were obtained by applying a triangular pulse on the devices, and the recovery time and drift-free characteristics were obtained by the paired pulses.

Bipolar Switching Characteristics of Transparent WOX-Based RRAM for Synaptic Application and Neuromorphic Engineering

In this work, we evaluate the resistive switching (RS) and synaptic characteristics of a fully transparent resistive random-access memory (T-RRAM) device based on indium-tin-oxide (ITO) electrodes. Here, we fabricated ITO/WO_X/ITO capacitor structure and incorporated DC-sputtered WO_X as the switching layer between the two ITO electrodes. The device shows approximately 77% (including the glass substrate) of optical transmittance in visible light and exhibits reliable bipolar switching behavior. The current-voltage (I-V) curve is divided into two types: partial and full curves affected by the magnitude of the positive voltage during the reset process. In the partial curve, we confirmed that the retention could be maintained for more than 10⁴ s and the endurance for more than 300 cycles could be stably secured. The switching mechanism based on the formation/rupture of the filament is further explained through the extra oxygen vacancies provided by the ITO electrodes. Finally, we examined the responsive potentiation and depression to check the synaptic characteristics of the device. We believe that the transparent WO_X-based RRAM could be a milestone for neuromorphic devices as well as future non-volatile transparent memory.

Effect of Post-Annealing on Barrier Modulations in Pd/IGZO/SiO2/p+-Si Memristors

In this article, we study the post-annealing effect on the synaptic characteristics in Pd/IGZO/SiO₂/p⁺-Si memristor devices. The O-H bond in IGZO films affects the switching characteristics that can be controlled by the annealing process. We propose a switching model based on using a native oxide as the Schottky barrier. The barrier height is extracted by the conduction mechanism of thermionic emission in samples with different annealing temperatures. Additionally, the change in conductance is explained by an energy band diagram including trap models. The activation energy is obtained by the depression curve of the samples with different annealing temperatures to better understand the switching mechanism. Moreover, our results reveal that the annealing temperature and retention can affect the linearity of potentiation and depression. Finally, we investigate the effect of the annealing temperature on the recognition rate of MNIST in the proposed neural network.

Physically Transient, Flexible, and Resistive Random Access Memory Based on Silver Ions and Egg Albumen Composites

Organic-resistance random access memory has high application potential in the field of next-generation green nonvolatile memory. Because of their biocompatibility and environmental friendliness, natural biomaterials are suitable for the fabrication of biodegradable and physically transient resistive switching memory devices. A flexible memory device with physically transient properties was fabricated with silver ions and egg albumen composites as active layers, which exhibited characteristics of write-once-read-many-times (WORM), and the incorporation of silver ions improved the ON/OFF current ratio of the device. The device can not only complete the logical operations of "AND gate" and "OR gate", but its active layer film can also be dissolved in deionized water, indicating that it has the characteristics of physical transients. This biocompatible memory device is a strong candidate for a memory element for the construction of transient electronic systems.

Multi-level Cells and Quantized Conductance Characteristics of Al2O3-Based RRAM Device for Neuromorphic System

Recently, various resistance-based memory devices are being studied to replace charge-based memory devices to satisfy high-performance memory requirements. Resistance random access memory (RRAM) shows superior performances such as fast switching speed, structural scalability, and long retention. This work presented the different filament control by the DC voltages and verified its characteristics as a synaptic device by pulse measurement. Firstly, two current-voltage (I-V) curves are characterized by controlling a range of DC voltages. The retention and endurance for each different I-V curve were measured to prove the reliability of the RRAM device. The detailed voltage manipulation confirmed the characteristics of multi-level cell (MLC) and conductance quantization. Lastly, synaptic functions such as potentiation and depression, paired-pulse depression, excitatory post-synaptic current, and spike-timing-dependent plasticity were verified. Collectively, we concluded that Pt/Al₂O₃/TaN is appropriate for the neuromorphic device.

Transformed Filaments by Oxygen Plasma Treatment and Improved Resistance State

The simple structure and operation method of resistive random-access memory (RRAM) has attracted attention as next-generation memory. However, as it is greatly influenced by the movement of oxygen atoms during switching, it is essential to minimize the damage and adjust the defects. Here, we fabricated an ITO/SnO_X/TaN device and investigated the performance improvement with the treatment of O₂ plasma. Firstly, the change in the forming curve was noticeable, and the defect adjustment was carried out effectively. By comparing the I-V curves, it was confirmed that the resistance increased and the current was successfully suppressed, making it suitable for use as a low-power consumption device. Retention of more than 10⁴ s at room temperature was measured, and an endurance of 200 cycles was performed. The filaments' configuration was revealed through the depth profile of X-ray photoelectron spectroscopy (XPS) and modeled to be visually observed. The work with plasma treatment provides a variety of applications to the neuromorphic system that require a low-current level.

Review of electrical stimulus methods of in situ transmission electron microscope to study resistive random access memory

Resistive random access memory (RRAM) devices have been demonstrated to be a promising solution for the implementation of a neuromorphic system with high-density synapses due to the simple device structure, nanoscale dimension, high switching speed, and low power consumption. Various electrical stimuli applied to RRAM devices could cause various working modes of the bionic synapses. The application of RRAM devices needs to understand the micromechanism of the resistive switching process, which is inseparable from advanced characterization techniques. In situ transmission electron microscopy (TEM) with high-resolution imaging and versatile external fields plays an important role in the static characterization and dynamic manipulation of nanoscale devices. Focused on in situ TEM techniques, this review article introduces in situ TEM setups and the corresponding sample fabrication process for RRAM research. Then, the electrical stimulating methodologies including pulse and direct current voltage applied to RRAM are introduced, followed by the summary of electron holography to characterize the electrical potential distribution. By applying various electrical stimuli to the RRAM samples, the working mode of bionic synapses could be changed according to the requirement. Finally, the outlook of the RRAM study with in situ TEM is proposed. This review demonstrates the electrical stimulus capability of in situ TEM to understand the physical mechanism of various types of RRAM devices.

Self-Powered Resistance-Switching Properties of Pr0.7Ca0.3MnO3 Film Driven by Triboelectric Nanogenerator

As one of the promising non-volatile memories (NVMs), resistive random access memory (RRAM) has attracted extensive attention. Conventional RRAM is deeply dependent on external power to induce resistance-switching, which restricts its applications. In this work, we have developed a self-powered RRAM that consists of a Pr0.7Ca0.3MnO3 (PCMO) film and a triboelectric nanogenerator (TENG). With a traditional power supply, the resistance switch ratio achieves the highest switching ratio reported so far, 9 × 107. By converting the mechanical energy harvested by a TENG into electrical energy to power the PCMO film, we demonstrate self-powered resistance-switching induced by mechanical movement. The prepared PCMO shows excellent performance of resistance switching driven by the TENG, and the resistance switch ratio is up to 2 × 105, which is higher than the ones ever reported. In addition, it can monitor real-time mechanical changes and has a good response to the electrical signals of different waveforms. This self-powered resistance switching can be induced by random movements based on the TENG. It has potential applications in the fields of self-powered sensors and human-machine interaction.

Resistive Switching and Synaptic Characteristics in ZnO/TaON-Based RRAM for Neuromorphic System

We fabricated an ITO/ZnO/TaON/TaN device as nonvolatile memory (NVM) with resistive switching for complementary metal-oxide-semiconductor (CMOS) compatibility. It is appropriate for the age of big data, which demands high speed and capacity. We produced a TaON layer by depositing a ZnO layer on a TaN layer using an oxygen-reactive radio frequency (RF) sputtering system. The bi-layer formation of ZnO and TaON interferes with the filament rupture after the forming process and then raises the current level slightly. The current levels were divided into high- and low-compliance modes. The retention, endurance, and pulse conductance were verified with a neuromorphic device. This device was stable and less consumed when it was in low mode rather than high mode.

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al₂O₃ interlayer between the HfO₂ switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO₂/Al₂O₃/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 10⁵ and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be - 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 10⁵ AC cycles, and a retention time of 10⁴ s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.

Stable Resistive Switching in ZnO/PVA:MoS2 Bilayer Memristor

Reliability of nonvolatile resistive switching devices is the key point for practical applications of next-generation nonvolatile memories. Nowadays, nanostructured organic/inorganic heterojunction composites have gained wide attention due to their application potential in terms of large scalability and low-cost fabrication technique. In this study, the interaction between polyvinyl alcohol (PVA) and two-dimensional material molybdenum disulfide (MoS2) with different mixing ratios was investigated. The result confirms that the optimal ratio of PVA:MoS2 is 4:1, which presents an excellent resistive switching behavior. Moreover, we propose a resistive switching model of Ag/ZnO/PVA:MoS2/ITO bilayer structure, which inserts the ZnO as the protective layer between the electrode and the composite film. Compared with the device without ZnO layer structure, the resistive switching performance of Ag/ZnO/PVA:MoS2/ITO was improved greatly. Furthermore, a large resistive memory window up to 104 was observed in the Ag/ZnO/PVA:MoS2/ITO device, which enhanced at least three orders of magnitude more than the Ag/PVA:MoS2/ITO device. The proposed nanostructured Ag/ZnO/PVA:MoS2/ITO device has shown great application potential for the nonvolatile multilevel data storage memory.

Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology

The family of two-dimensional (2D) materials composed of atomically thin layers connected via van der Waals interactions has attracted much curiosity due to a variety of intriguing physical, optical, and electrical characteristics. The significance of analyzing statistics on electrical devices and circuits based on 2D materials is seldom underestimated. Certain requirements must be met to deliver scientific knowledge that is beneficial in the field of 2D electronics: synthesis and fabrication must occur at the wafer level, variations in morphology and lattice alterations must be visible and statistically verified, and device dimensions must be appropriate. The authors discussed the most recent significant concerns of 2D materials in the provided prose and attempted to highlight the prerequisites for synthesis, yield, and mechanism behind device-to-device variability, reliability, and durability benchmarking under memristors characteristics; they also indexed some useful approaches that have already been reported to be advantageous in large-scale production. Commercial applications, on the other hand, will necessitate further effort.

Improved resistive switching characteristics of a multi-stacked HfO2/Al2O3/HfO2 RRAM structure for neuromorphic and synaptic applications: experimental and computational study

Atomic Layer Deposition (ALD) was used for a tri-layer structure (HfO2/Al2O3/HfO2) at low temperature over an Indium Tin Oxide (ITO) transparent electrode. First, the microstructure of the fabricated TaN/HfO2/Al2O3/HfO2/ITO RRAM device was examined by the cross-sectional High-Resolution Transmission Electron Microscopy (HRTEM). Then, Energy Dispersive X-ray Spectroscopy (EDS) was performed to probe compositional mapping. The bipolar resistive switching mode of the device was confirmed through SET/RESET characteristic plots for 100 cycles as a function of applied biasing voltage. An endurance test was performed for 100 DC switching cycles @0.2 V wherein; data retention was found up to 104 s. Moreover, for better insight into the charge conduction mechanism in tri-layer HfO2/Al2O3/HfO2, based on oxygen vacancies (VOX), total density of states (TDOS), partial density of states (PDOS) and isosurface three-dimensional charge density analysis was performed using WEIN2k and VASP simulation packages under Perdew-Burke-Ernzerhof _Generalized Gradient approximation (PBE-GGA). The experimental and theoretical outcomes can help in finding proper stacking of the active resistive switching (RS) layer for resistive random-access memory (RRAM) applications.