Resistive Random Access Memory Devices Research Articles

Design of gelatin-based bionic device for neural computing applications

This study introduces an approach to the development of resistive random-access memory (RRAM) devices by utilizing gelatin-based materials and incorporating an ultrathin Al2O3 interface layer fabricated using atomic layer deposition (ALD). The integration of the ALD technology for Al2O3 deposition ensures precise control over the layer thickness and quality, contributing to enhanced device uniformity and stability. The AGAI device can withstand more than 200 switching cycles while maintaining a high ON/OFF ratio exceeding 103, and it also successfully emulates synaptic plasticity characteristics, including long-term synaptic plasticity and short-term synaptic plasticity, as demonstrated by their responses to paired-pulse facilitation and paired-pulse depression, respectively, thereby validating their potential for neural computing applications. Additionally, the hybrid conduction mechanism in the AGAI device, involving aluminum and oxygen vacancy filaments, provides remarkable control over the switching process, leading to repeatability in synaptic characteristics and resistance states. These capabilities introduce new possibilities for the development of neuromorphic computing systems, which aim to mimic the functionality of the human brain in processing and storing information.

Development of a novel self-healing Zn(II)-metallohydrogel with wide bandgap semiconducting properties for non-volatile memory device application

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal–semiconductor-metal configuration and it’s charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability.

Metal Penetration and Grain Boundary in MoS2 Memristors

Abstract2D semiconductors have demonstrated outstanding switching performance in resistive random‐access memory (RRAM). Despite the proposed resistive switching (RS) mechanism involving the penetration of electrode metal atoms, direct observation of metal penetration in these van‐der‐Waals stacked 2D semiconductors remains absent. This study utilizes 2D molybdenum disulfide (MoS2) as the switching material, employing gold and multilayer graphene as electrodes. Through analysis of the switching characteristics of these RRAM devices, the pivotal role of grain boundaries and metal atoms is identify in achieving RS. High‐resolution transmission electron microscopy and energy‐dispersive X‐ray spectroscopy provide direct evidence of metal penetration into multilayer MoS2. This study offers valuable insights into the RS mechanism in memristors based on multilayer MoS2, providing guidance for designing and optimizing 2D material memristive devices.

Zeolitic-imidazolate framework (ZIF-67) based bipolar memristor for nonvolatile ReRAM application

In the realm of next-generation nonvolatile memories, there is a strong demand for resistive random access memory (ReRAM) devices that are affordable, stable, and simple to produce. Metal–organic frameworks (MOFs) have garnered extensive research attention across diverse fields due to their remarkable chemical adaptability, stability, and exceptional structural variability. In this work, a ReRAM device has been fabricated that incorporates a solution-processed thin film of zeolitic imidazolate framework (ZIF-67), a material of MOFs class, as the resistive switching (RS) layer. This Al/ZIF-67/ITO structured device possesses a consistent RS behavior with a high on/off resistance ratio of ∼104. The retention of low resistance state and high resistance state have been shown up to 104 s. Furthermore, the device also shows a consistent switching for 500 cycles of pulse switching signals of +6 V/−1.8 V (set/reset). Based on the impedance spectroscopy analysis, a filamentary switching mechanism has been established for the RS behavior of the device. The robust and enduring performance, coupled with the substantial on/off resistance ratio and high retention of the states, makes this device a promising candidate for nonvolatile ReRAM applications.

Optimization of the position of TaOx:N-based barrier layer in TaOx RRAM devices

Resistive Random-Access Memory (RRAM) presents a transformative technology for diverse computing and artificial intelligence applications. However, variability in the high resistance state (HRS) has proved to be a challenge, impeding its widespread adoption. This study focuses on optimizing TaOx-based RRAMs by strategically placing a nitrogen-doped TaOx barrier-layer (BL) to mitigate variability in the HRS. Through comprehensive electrical characterization and measurements, we uncover the critical influence of BL positioning on HRS variability and identify the optimal location of the BL to achieve a 2x lowering of HRS variability as well as an expanded range of operating voltages. Incremental reset pulse amplitude measurements show that the TaOx:N maintains a low HRS variability even at higher operating voltages when the position of the BL is optimized. Our findings offer insights into stable and reliable RRAM operation, highlighting the potential of the proposed BL to enhance the functionality of TaOx-based RRAMs and elevate overall device performance.

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis.

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

AbstractIn the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

Investigation on self-rectifying properties of Pt/HfO2/Ti with rivet-like structure based on ALD conformal technology

This work explores an architecture for nonvolatile resistive random-access memory (RRAM) systems. The study proposes a self-rectifying RRAM device utilizing a two-terminal 1R selector that harmonizes the gate control efficacy of transistors with the inherent simplicity of diode structures. A rivet-like HfO2-based RRAM array is meticulously constructed through atomic layer deposition (ALD), aiming to enhance device performance and retention stability. The conformal fabrication technique of ALD method is critical in achieving uniform coverage of isolation layers and precise electrode placement, which is instrumental in the fabrication of high-performance memory cells. Empirical analyses indicate significant improvements in rectification and ON/OFF ratios compared to existing RRAM models, bolstered by compatibility with established CMOS processes. It reveals that these advances are conducive to scalable, high-density memory integration, positioning the RRAM as a viable contender for future computational applications that require high efficiency and neuromorphic computing capabilities.

Stable and repeatable ZrO2 RRAM achieved by NiO barrier layer for negative set phenomenon elimination

Resistive random-access memory (RRAM) is a promising non-volatile memory technology due to its fast operation, low power consumption, and high reliability. However, the negative set phenomenon remains a challenge for RRAM, which can lead to the deterioration of device switching parameters. In this study, we successfully addressed this issue by inserting a NiO blocking layer between the ZrO2 and Ag top electrodes in ZrO2-based RRAM. In addition, the resistive switching characteristics of the device are significantly enhanced by the incorporation of a p-type oxide NiO layer. Compared to single-layer ZrO2 devices, the double-layer Ag/NiO/ZrO2/ITO RRAM exhibits improved cycling durability (>500 cycles) and good retention time (3 × 104s). Our analysis of the device conduction mechanism and proposed resistive switching model suggest that oxygen vacancies play a connecting role in the reset process, leading to failed reset behavior. Through the incorporation of a p-type semiconductor NiO layer into ZrO2-based RRAMs, the interference of oxygen vacancies on the reset process can be effectively impeded. This approach not only provides an effective resolution to the unforeseen negative set phenomenon in RRAM devices, but it also holds paramount significance for the advancement of high-performance RRAMs.

The study of lithographic variation in resistive random access memory

Reducing the process variation is a significant concern for resistive random access memory (RRAM). Due to its ultra-high integration density, RRAM arrays are prone to lithographic variation during the lithography process, introducing electrical variation among different RRAM devices. In this work, an optical physical verification methodology for the RRAM array is developed, and the effects of different layout parameters on important electrical characteristics are systematically investigated. The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments. The read resistance is more sensitive to the locations in the array (~30%) than SET/RESET voltage (<10%). The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%, whereas it reduces RRAM read resistance by 4×, resulting in a higher power and area consumption. As such, we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.

Realization of Multiple Synapse Plasticity by Coexistence of Volatile and Nonvolatile Characteristics of Interface Type Memristor.

Studies on neuromorphic computing systems are becoming increasingly important in the big-data-processing era as these systems are capable of energy-efficient parallel data processing and can overcome the present limitations owing to the von Neumann bottleneck. The Pt/WOx/ITO resistive random-access memory device can be used to implement versatile synapse functions because it possesses both volatile and nonvolatile characteristics. The gradual increase and decrease in the current of the Pt/WOx/ITO device with its uniform resistance state for endurance and retention enables additional synaptic applications that can be controlled using electric pulses. If the volatile and nonvolatile device properties are set through rehearsal and forgetting processes, the device can emulate various synaptic behaviors, such as potentiation and depression, paired-pulse facilitation, post-tetanic potentiation, image training, Hebbian learning rules, excitatory postsynaptic current, and Pavlov's test. Furthermore, reservoir computing can be implemented for applications such as pattern generation and recognition. This emphasizes the various applications of future neuromorphic devices, demonstrating the various favorable characteristics of pulse-enhanced Pt/WOx/ITO devices.

Enhancing the Uniformity of a Memristor Using a Bilayer Dielectric Structure.

Resistive random access memory (RRAM) holds great promise for in-memory computing, which is considered the most promising strategy for solving the von Neumann bottleneck. However, there are still significant problems in its application due to the non-uniform performance of RRAM devices. In this work, a bilayer dielectric layer memristor was designed based on the difference in the Gibbs free energy of the oxide. We fabricated Au/Ta2O5/HfO2/Ta/Pt (S3) devices with excellent uniformity. Compared with Au/HfO2/Pt (S1) and Au/Ta2O5/Pt (S2) devices, the S3 device has a low reset voltage fluctuation of 2.44%, and the resistive coefficients of variation are 13.12% and 3.84% in HRS and LRS, respectively, over 200 cycles. Otherwise, the bilayer device has better linearity and more conductance states in multi-state regulation. At the same time, we analyze the physical mechanism of the bilayer device and provide a physical model of ion migration. This work provides a new idea for designing and fabricating resistive devices with stable performance.

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic branches facilitate temporal feature detection via synaptic delays that enable coincidence detection (CD) mechanisms. Spiking neural networks highlight the significance of delays for spatio-temporal pattern recognition in feed-forward networks, eliminating the need for recurrent structures. Here, we introduce DenRAM, a novel analog electronic feed-forward spiking neural network with dendritic compartments. Utilizing 130 nm technology integrated with resistive RAM (RRAM), DenRAM incorporates both delays and synaptic weights. By configuring RRAMs to emulate bio-realistic delays and exploiting their heterogeneity, DenRAM mimics synaptic delays and efficiently performs CD for pattern recognition. Hardware-aware simulations on temporal benchmarks show DenRAM’s robustness against hardware noise, and its higher accuracy over recurrent networks. DenRAM advances temporal processing in neuromorphic computing, optimizes memory usage, and marks progress in low-power, real-time signal processing

Compensation architecture design utilizing residual resource to mitigate impacts of nonidealities in RRAM-based computing-in-memory chips

Resistive random access memory (RRAM) is a promising technology for energy-efficient in-memory computing. However, due to technology limits, RRAM device faces a series of reliability issues. Deep neural network (DNN) computing based on RRAM suffers from accuracy degradation. On the one hand, offline DNN training solutions are difficult to fully consider and simulate all nonidealities. Worse still, new error or nonideality may come up with the usage of RRAM, which further deteriorates the effectiveness of offline training. On the other hand, online training poses great challenges on programming overhead and device lifetime. The iterative write-verify technique to program multi-bit RRAM cells prolongs write latency more than 10× longer than read latency. To overcome these issues, we propose a compensation architecture and training designs to mitigate the realistic accuracy loss in RRAM chips with negligible hardware resource for RRAM-based DNN computing. Firstly, we add trainable compensation channels in crossbars utilizing the residual resource after original weight mapping. Secondly, an offline training procedure with computing output from hardware is triggered to settle down appropriate weight value in compensation channels. Experimental results demonstrate that the proposed design can guarantee ≤ 0.8% loss of accuracy in DNN even when nonidealities reduce the original accuracy down to ≤73%.

Flexible resistive memory device with egg-albumen/HfO x hybrid bilayer: fabrication and modeling of its switching variations

Egg-albumen, a natural polymer, in bilayer combination with ultrathin HfO x is explored as an active switching layer component in flexible resistive random access memory devices. The fabricated devices have shown excellent switching characteristics with a current on/off ratio of greater than 104, stable retention of both low resistance and high resistance states, reliable multiple cycle switching, and very low switching power (with set power as 0.5 µW and reset power as 3.1 mW). To investigate the electro-mechanical stability, devices were bent with different bending radii and it was found that negligible degradation in device performance was observed until a 5 mm bending radius. Furthermore, a simple mathematical model is used to simulate the devices’ characteristics and the values of fitting parameters were extracted with a root mean square error of less than 4.5%. Moreover, a switching variation was introduced by utilizing variations of the physical parameters, and a near practical physics based mathematical device model was demonstrated which can enable the strengthening of simulation capabilities for exploration of unique flexible resistive memory devices and related circuits.

Atomic layer annealing for spatial tailoring in sub-4 nm AlN RRAM devices with low-voltage operation

This article investigates the spatial tailoring of nitrogen vacancies by atomic layer annealing (ALA) on resistive random access memory (RRAM) devices with an AlN resistive switching layer. The ALA technique involves the in-situ layer-by-layer He/Ar plasma treatment in the cycle of atomic layer deposition for AlN. The AlN RRAM devices prepared with the ALA treatment near the TiN electrode have significantly superior uniformity of the operation voltages and the switching resistances, along with a lower cycle-to-cycle variation in endurance. This enhancement could be attributed to the relatively stable location of rupture and formation of conductive filaments, as a result of the film densification and the suppression of nitrogen vacancies in the AlN region treated with the ALA process. The AlN RRAM devices also demonstrate excellent retention of more than 106 seconds at 125°C. In addition, the ultra-thin 3.3 nm AlN switching layer contributes to low operation voltages of AlN RRAM. The outcomes manifest that the ALA technique could effectively improve the properties of nanoscale materials through precise spatial tailoring, with an accuracy up to atomic scale.

Dielectric Properties and Carrier Transport Mechanism in Annealed HfOx‐Based Resistive Random Access Memory Devices

The diversity of the carrier transport mechanism in the resistive switching process remains a significant obstacle for the design and application of resistive random access memory (RRAM) devices. In this study, the influence of annealing on the dielectric properties of the Ti/HfOx/Pt RRAM device and its associated carrier transport mechanism is investigated. The results reveal that the current conduction in both annealed and unannealed devices is primarily attributed to grain boundary (GB) relaxation, with only the GB acting as a depletion layer that induces dielectric relaxation. Furthermore, annealing decreases the interfacial polarization and changes the relaxation time at the GB. In addition, the charge carriers exhibit nearest‐neighbor hopping conduction at a higher temperature in Ti/HfOx/Pt RRAM devices, whereas variable‐range‐hopping conduction occurs at lower temperatures. Long‐range charge carrier transport also plays a significant role in the conduction process. To offer a deeper understanding of the conduction processes in the Ti/HfOx/Pt RRAM device, a carrier transport model is proposed, which provides valuable insights into the intricate mechanisms governing conduction in these devices.

Atomic‐Scale Phase Transformation in Perovskite LaCoOx Resistive Switching Memristive Devices

Resistive random‐access memory (RRAM) is considered the next‐generation nonvolatile memory owing to its simplicity, low power consumption, and high storage density. Resistive switching (RS) occurs in a wide range of materials among the transition metal oxides. Herein, an epitaxial ternary metal oxide layer, LaCoOx (LCO), grown on Nb‐doped SrTiO3 substrates, is utilized as an RRAM device. When voltage is applied, it exhibits excellent RS behavior. More than 900 cycles are obtained, and the retention time reaches up to 104 s. To investigate the RS behavior, high‐resolution transmission electron microscopy and atomic‐scale scanning transmission electron microscopy are used to observe the structural evolution and oxygen ion migration in LCO. The structure exhibits a perovskite–brownmillerite topotactic phase transformation from LaCoO2.5 or LaCoO2.67 to the LaCoO3 conductive regions. The reversible transition between the low‐resistance states and high‐resistance states enables the RS mechanism. Additionally, the valence states are confirmed using high‐resolution X‐ray photoelectron spectroscopy. This study not only illustrates the oxygen‐ion migration mechanism of LCO but also demonstrates its suitability for RRAM applications.

Effect of Electrochemically Active Top Electrode Materials on Nanoionic Conductive Bridge Y2O3 Random-Access Memory.

Herein, sol-gel-processed Y2O3 resistive random-access memory (RRAM) devices were fabricated. The top electrodes (TEs), such as Ag or Cu, affect the electrical characteristics of the Y2O3 RRAM devices. The oxidation process, mobile ion migration speed, and reduction process all impact the conductive filament formation of the indium-tin-oxide (ITO)/Y2O3/Ag and ITO/Y2O3/Cu RRAM devices. Between Ag and Cu, Cu can easily be oxidized due to its standard redox potential values. However, the conductive filament is easily formed using Ag TEs. After triggering the oxidation process, the formed Ag mobile metal ions can migrate faster inside Y2O3 active channel materials when compared to the formed Cu mobile metal ions. The fast migration inside the Y2O3 active channel materials successfully reduces the SET voltage and improves the number of programming-erasing cycles, i.e., endurance, which is one of the nonvolatile memory parameters. These results elucidate the importance of the electrochemical properties of TEs, providing a deeper understanding of how these factors influence the resistive switching characteristics of metal oxide-based atomic switches and conductive-metal-bridge-filament-based cells.

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations.

Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate neuronal behaviors. RRAM devices excel in terms of their compact size, fast switching capabilities, high ON/OFF ratio, and low energy consumption, among other advantages. This review focuses on the multifaceted aspects of RRAM devices and their application to brain-inspired computing. The review begins with a brief overview of the essential biological concepts that inspire the development of bio-mimetic computing architectures. It then discusses the various types of resistive switching behaviors observed in RRAM devices and the detailed physical mechanisms underlying their operation. Next, a comprehensive discussion on the diverse material choices adapted in recent literature has been carried out, with special emphasis on the benchmark results from recent research literature. Further, the review provides a holistic analysis of the emerging trends in neuromorphic applications, highlighting the state-of-the-art results utilizing RRAM devices. Commercial chip-level applications are given special emphasis in identifying some of the salient research results. Finally, the current challenges and future outlook of RRAM-based devices for neuromorphic research have been summarized. Thus, this review provides valuable understanding along with critical insights and up-to-date information on the latest findings from the field of resistive switching devices towards brain-inspired computing.