Resistive Random Access Memory Research Articles

Scanning Thermal Microscopy Method for Self-Heating in Nonlinear Devices and Application to Filamentary Resistive Random-Access Memory.

Devices with a highly nonlinear resistance-voltage relationship are candidates for neuromorphic computing, which can be achieved by highly temperature dependent processes like ion migration. To explore the thermal properties of such devices, Scanning Thermal Microscopy (SThM) can be employed. However, due to the nonlinearity, the high resolution and quantitative method of AC-modulated SThM cannot readily be used. To this end, an extended nonequilibrium scheme for temperature measurement using SThM is proposed, with which the self-heating of nonlinear devices is studied without the need for calibrating the tip-sample contact for a specific material combination, geometry or roughness. Both a DC and an AC voltage are applied to the device, triggering a periodic temperature rise, which enables the simultaneous calculation of the tip-sample thermal resistance and the device temperature rise. The method is applied to HfO2-based RRAM devices, in which the kinetic processes of filamentary switching are governed by temperature. We image temperature and propagation of thermal waves and extract properties like the number of current filaments, thermal confinement and thermal cross-talk.

Characterization of Graphene and Indium Tin Oxynitride Thin Films for Use as Layers in Resistive Memories

Graphene is a two‐dimensional material, comprising sp²‐hybridized carbon atoms organized in a hexagonal lattice. An application of this material lies in resistive random‐access memories (ReRAMs). In this work, resistive memory devices with three layers were manufactured. Graphene thin films were deposited by dip coating on indium tin oxynitride (ITON) electrodes. For the ITON contacts, indium tin oxide was evaporated in an oxygen atmosphere and subsequently annealed with a nitrogen flow atmosphere in a conventional furnace at different temperatures: 350, 400, 500 and 550 °C. The evaporation technique was employed to produce aluminum as top contacts. For graphene and ITON characterization, structural analysis of Raman, electrical measurements through Hall effect and optical UV – Vis measurements were performed. Resistive memories were also characterized through I × V analysis. The devices can be characterized as unipolar, with threshold switching attributed to the formation of oxygen vacancies. ITON was deposited with a substantial layer, containing oxygen, while graphene, with an average thickness of 30 nm measured by profilometer, forms a thinner layer. During the voltage increase and subsequent decrease, it is postulated that the oxygen vacancies within this material may migrate towards the intermediate layer, graphene, due to its reduced thickness and defects.

Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models

Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation. To address this challenge, we propose RePACK, a threefold data protection scheme that safeguards neural network input, weight, and structural information. It utilizes a bipartite-sort coding scheme to store data with a fully on-chip physical unclonable function. Experimental results demonstrate the effectiveness of increasing enumeration complexity to 5.77 × 1075 for a 128-column compute-in-memory core. We further implement and evaluate a RePACK computing system on a 40 nm resistive memory compute-in-memory chip. This work represents a step towards developing safe, robust, and efficient edge neural network accelerators. It potentially serves as the hardware infrastructure for edge devices in federated learning or other systems.

Optoelectronic array of photodiodes integrated with RRAMs for energy-efficient in-sensor computing

The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

Combined feature of enhanced stability and multi-level switching observed in TiN/Ta2O5/Ag-NPs/ITO/PET structure.

Both stability and multi-level switching are crucial performance aspects for resistive random-access memory (RRAM), each playing a significant role in improving overall device performance. In this study, we successfully integrate these two features into a single RRAM configuration by embedding Ag-nanoparticles (Ag-NPs) into the TiN/Ta2O5/ITO structure. The device exhibits substantially lower switching voltages, a larger switching ratio, and multi-level switching phenomena compared to many other nanoparticle-embedded devices. We attribute it to the embedded Ag-NPs effectively switching the mechanism of conductive filaments and the controlled distribution of Ag-NPs facilitates the occurrence of multi-level switching. Additionally, the fabricated structure demonstrated an impressive optical transmittance of nearly 85%. Undoubtedly, this combined feature of RRAM not only enhances stability but also enables multi-level switching, thereby demonstrating an approach to fabricating versatile and practical electronic devices aimed at boosting storage capacity and speed.&#xD.

Combined feature of enhanced stability and multi-level switching observed in TiN/Ta2O5/Ag-NPs/ITO/PET structure.

Both stability and multi-level switching are crucial performance aspects for resistive random-access memory (RRAM), each playing a significant role in improving overall device performance. In this study, we successfully integrate these two features into a single RRAM configuration by embedding Ag-nanoparticles (Ag-NPs) into the TiN/Ta2O5/ITO structure. The device exhibits substantially lower switching voltages, a larger switching ratio, and multi-level switching phenomena compared to many other nanoparticle-embedded devices. We attribute it to the embedded Ag-NPs effectively switching the mechanism of conductive filaments and the controlled distribution of Ag-NPs facilitates the occurrence of multi-level switching. Additionally, the fabricated structure demonstrated an impressive optical transmittance of nearly 85%. Undoubtedly, this combined feature of RRAM not only enhances stability but also enables multi-level switching, thereby demonstrating an approach to fabricating versatile and practical electronic devices aimed at boosting storage capacity and speed.&#xD.

The show must go on: a reliability assessment platform for resistive random access memory crossbars.

Resistive random access memory (ReRAM) holds promise for building computing-in-memory (CIM) architectures to execute machine learning (ML) applications. However, existing ReRAM technology faces challenges such as cell and cycle variability, read-disturb and limited endurance, necessitating improvements in devices, algorithms and applications. Understanding the behaviour of ReRAM technologies is crucial for advancement. Existing platforms can either only characterize single cells and do not support CIM operations, or lack a comprehensive software stack for simple system integration. This article introduces NeuroBreakoutBoard (NBB), a versatile, integrable and portable instrumentation platform for ReRAM crossbars. The platform features a software stack enabling experiments via Python from a host PC. In a case study, we demonstrate the capabilities of NBB by conducting diverse experiments on TiN/Ti/HfO2/TiN cells. Our results show that NBB can characterize individual cells and perform CIM operations with a relative measurement error below 2%.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.

Memristive ternary Łukasiewicz logic based on reading-based ratioed resistive states (3R).

The thirst for more efficient computational paradigms has reignited interest in computation in memory (CIM), a burgeoning topic that pivots on the strengths of more versatile logic systems. Surging ahead in this innovative milieu, multi-valued logic systems have been identified as possessing the potential to amplify storage density and computation efficacy. Notably, ternary logic has attracted widespread research owing to its relatively lower computational and storage complexity, offering a promising alternative to the traditional binary logic computation. This study provides insight into the feasibility of ternary logic in the CIM domain using resistive random-access memory (ReRAM) devices. Its multi-level programming capability making it an ideal conduit for the integration of ternary logic. We focus on ternary Łukasiewicz logic because its computational characteristics are highly suitable for mapping logic values with input and output signals. This approach is characterized by voltage-reading-based output for ease of subsequent utilization and computation and validated in 1T1R crossbar arrays in an integrated ReRAM chip (Memory Advanced Demonstrator 200 mm). In addition, the effect of variability of memristive devices on logical computation and the potential for parallel operation are also investigated.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.

Concealable physical unclonable function generation and an in-memory encryption machine using vertical self-rectifying memristors.

The importance of hardware security increases significantly to protect the vast amounts of private data stored on edge devices. Physical unclonable functions (PUFs) are gaining prominence as hardware security primitives due to their ability to generate true random digital keys by exploiting the inherent randomness of the physical devices. Traditional approaches, however, require significant data movement between memory units and PUF generation circuits to perform encryption, presenting considerable energy efficiency and security challenges. This study introduces an innovative approach where PUF key generation and encryption are accomplished in the same vertically integrated resistive random access memory (V-RRAM), alleviating the data movement issue. The proposed V-RRAM encryption machine offers concealable PUFs, high area efficiency, and multi-thread data handling using parallel XOR logic operations. The encryption machine is compared with other machines, demonstrating the highest spatiotemporal cost-effectiveness.

Iodine management of crown ether in perovskite resistive random access memories for improved switching ratio and long-term stability

Iodine management of crown ether in perovskite resistive random access memories for improved switching ratio and long-term stability

Optimizing of Resistive Random Access Memory to Increase Its Practicability

The progress in Integrated Circuit Technology, has promoted the development of emerging technologies like AI and Iot. It also requires the memory technology a high standard. Resistive random-access memory (RRAM) has the advantages of lower power consumption, fast speed and owns a strong compressibility, and it is regarded as the most promising NVM in next generation. This review explains basic principle of RRAM, several switching mechanisms, and different materials of the dielectric layer, then compares performance of RRAM in different materials. The RRAM that is completed can replace Flash memory due to its function currently, and can be applied to different fields like Neural Network and IoT. However, the experimental data of RRAM is still not stable. Till now, feasible methods include Defect Engineering, Nanostructure or looking for better materials. Ion doping is one of Defect Engineering, it has been used for years and is proven to be useful on improving the stability and memory window of RRAM. As the fundamental in the development of electronics, the optimization of RRAM definitely has great influence on it. If RRAM can be widely used in the future, the development of new technologies will inevitably be astonishing.

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.

Inkjet Printed Metal–Organic Frameworks for Non‐Volatile Memory Devices Suitable for Printed RRAM

AbstractInkjet printing has emerged as a promising technique for patterning functional materials, offering significant advantages over traditional subtractive thin‐film methods. Its versatility enables the structuring of various materials, expanding application ranges and minimizing waste through additive manufacturing. However, the limited availability of functional material‐based inks suitable for inkjet printing presents challenges in ink formulation. HKUST‐1, a 3D cubic metal–organic frameworks (MOFs) comprised of copper(II) ions coordinated to benzene‐1,3,5‐tricarboxylate (BTC) organic linkers, known for its porosity and tunability, have potential to enhance inkjet‐printed devices. This study combines inkjet printing and evaporation‐induced crystallization to structure HKUST‐1, marking the first demonstration of nanocrystalline HKUST‐1 integrated into a printed electronic device, specifically a memristor, where the MOF is prepared by inkjet printing of a precursor solution. Memristors, which change their resistance based on the external stimuli history, enabling the construction of resistive random‐access memory (RRAM). The fabricated memristors in this study exhibit notable properties: low forming voltage, an Roff/Ron ratio of 104, a retention time of 600 s, and endurance exceeding 60 write and erase cycles. This research highlights the potential of integrating MOFs into inkjet printing, unlocking broader application possibilities, and advancing additive manufacturing for functional materials.

Enhancing Resistive Switching in AlN-Based Memristors Through Oxidative Al2O3 Layer Formation: A Study on Preparation Techniques and Performance Impact.

Aluminum nitride (AlN) with a wide band gap (approximately 6.2 eV) has attractive characteristics, including high thermal conductivity, a high dielectric constant, and good insulating properties, which are suitable for the field of resistive random access memory. AlN thin films were deposited on ITO substrate using the radio-frequency magnetron sputtering technique. Al's and Au's top electrodes were deposited on AlN thin films to make a Au/Al/AlN/ITO sandwich structure memristor. The effects of the Al2O3 film on the on/off window and voltage characteristics of the device were investigated. The deposition time and nitrogen content in the sputtering atmosphere were changed to adjust the thickness and composition of AlN films, respectively. The possible mechanism of resistive switching was examined via analyses of the electrical resistive switching characteristics, forming voltage, and switching ratio.

Uniformity, Linearity, and Symmetry Enhancement in TiOx/MoS2-xOx Based Analog RRAM via S-Vacancy Confined Nanofilament.

Due to the stochastic formation of conductive filaments (CFs), analog resistive random-access memory (RRAM) struggles to simultaneously achieve low variability, high linearity, and symmetry in conductance tuning, thus complicating on-chip training and limiting versatility of RRAM based computing-in-memory (CIM) chips. In this study, we present a simple and effective approach using monolayer (ML) MoS2 as interlayer to control the CFs formation in TiOx switching layer. The limited S-vacancies (Sv) in MoS2-xOx interlayer can further confine the position, size, and quantity of CFs, resulting in a highly uniform and symmetrical switching behavior. The set and reset voltages (Vset and Vreset) in TiOx/MoS2-xOx based RRAM are symmetric, with cycle-to-cycle variations of 1.28% and 1.7%, respectively. Moreover, high conductance tuning linearity and 64-level switching capabilities are achieved, which facilitate high accuracy (93.02%) on-chip training. This method mitigates the device nonidealities of analog RRAM through Sv confined CFs, accelerating the development of RRAM based CIM chips.

Enhanced performance of self-selective RRAM devices in V/TaOx/Pt structure

Abstract The performance of resistive random-access memory devices with vanadium as the top electrode and different TaOx insulating layer is investigated in this paper. The results indicate that the VO2 oxide layer generated by the oxidation of the vanadium electrode can serve as inherent selector, without the need for additional series selectors required in conventional methods. A large amount of oxygen vacancy migration was limited in the double insulating layers, enabling the device to achieve stable Self-Selective resistive random-access memory performance. This indicated that the advantage of the double insulation layers lay in manipulating the migration of oxygenions and vacancies. Endurance tests showed no degradation over 103 cycles, and the device maintained stability after 20,000 pulse applications. The subthreshold swing of the selector was less than 42 mV/dec, and the switching time was less than 2 μs. This research presents a promising advancement in resistive random-access memory technology with potential for high-density memory integration and reliable performance.

Self‐Rectifying Switching Memory Based on HfOx/FeOx Semiconductor Heterostructure for Neuromorphic Computing

AbstractSneak‐path current is one of the biggest barriers for large‐scale passive memristor array integration. An ideal self‐rectifying resistance random access memory (SR‐RRAM) is a desirable solution but it has not been demonstrated today for optimizing comprehensive indexes for neuromorphic computing. The HfOx/FeOx semiconductor heterojunction SR‐RRAM with a robust self‐rectifying switching behavior featured by an average rectifying ratio (≈104), high resistance ratio (>106), high cycling endurance (>104 cycles), high computing precision (>6 bits) and synaptic plasticity such as paired‐pulse facilitation (PPF) and the spike‐timing‐dependent plasticity (STDP) for artificial intelligence recognition is developed using the unidirectional conductivity feature of p‐n junction. The electron hopping, tunneling, and blocking in this semiconductor heterojunction that is verified by the energy band mode based on UV photoelectron spectroscopy (UPS) technology and low‐energy inverse photoelectron spectroscopy (LEIPS) and in situ high resolution transmission electron microscopy (HR‐TEM) observation plays a dominant role in the self‐rectifying analog switching behaviors. This work provides energy‐band engineering for the large‐scale memristor array integration, representing a significant advancement in hardware for neuromorphic computing.

Multilevel Resistive Switching Dynamics by Controlling Phase and Self-Assembled Nanochannels in HfO2.

A resistive switching device with precise control over the formation of conductive filaments (CF) holds immense potential for high-density memory arrays and atomic-scale in-memory computing architectures. While ion migration and electrochemical switching mechanisms are well understood, controlling the evolution of CF remains challenging for practical resistive random-access memory (RRAM) deployment. This study introduces a systematic approach to modulate oxygen vacancies (OV) in HfO2 films of Ag/HfO2/Pt-based RRAM devices by controlling the substrate temperature. At 300°C, the HfO2 film exhibits a dominant monoclinic phase with maximum OV concentration, which plays a key role in achieving optimal multilevel resistive switching behavior. Self-assembled nanochannels in the HfO2 films guide CF evolution, and the diffusion of Ag at inside these films suggests a synergistic interplay between OV and Ag⁺ ion migration for reseting the voltage-controlled resistive states. This approach addresses the endurance/retention trade-off with an impressive Ron/Roff ratio of ≈8000while demonstrating growth temperature-driven OV modulation as a tool for multi-bit data storage. These findings provide a blueprint for developing high-performance oxide-based RRAM devices and offer valuable insights into multilevel resistive switching mechanisms, paving the way for future low-power, high-density memory technologies.

Investigation of solution-processed tungsten disulfide as switching layer in flexible resistive memory devices for performance and stability

Abstract Solution-processed tungsten disulfide (WS2) is demonstrated as a promising resistive switching layer for flexible resistive random access memory (RRAM) devices. For this study, WS2 nanoparticle solution was prepared by liquid exfoliation process from WS2 powder and comprehensive material investigation was performed to understand the suitability for device fabrication through morphologies and electronic behavior. Devices were fabricated on indium tin oxide (ITO) coated flexible polyethylene terephthalate (PET) with ITO acting as bottom electrode (BE) and silver (Ag) deposited as top electrode (TE) with thin film of WS2 prepared by spin coating as an active layer between electrodes. These fabricated flexible RRAM devices exhibited resistive switching characteristics with low operating voltages (V SET ∼0.5 V and V RESET ∼−1.4 V) over 100 consecutive cycles, along with impressive retention time of ∼104 s and high I ON/I OFF of ∼104. The performance variation of these devices was also investigated upon bending at radii of 12 mm and 5 mm indicating consistent switching, however a decay in LRS was observed after 250 s upon investigation of retention. These findings suggest that solution-processed thin films of WS2 nanomaterial can act as a promising switching layer for flexible electronics.

Signal Integrity Analysis for Resistive Random Access Memory Crossbar Array by Compact Model With Consideration of Electro-Thermal Coupling Effects

Signal Integrity Analysis for Resistive Random Access Memory Crossbar Array by Compact Model With Consideration of Electro-Thermal Coupling Effects