Search Results (176)

In this study, we investigate the impact of O₂ plasma treatment on the performance of Al/TaO_X/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O₂-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O₂-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current–voltage (I–V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 10³ s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device. Full article

In computer vision tasks, the ability to remove rain from a single image is a crucial element to enhance the effectiveness of subsequent high-level tasks in rainy conditions. Recently, numerous data-driven single-image deraining techniques have emerged, primarily relying on paired images (i.e., in a supervised manner). However, when dealing with real deraining tasks, it is common to encounter unpaired images. In such scenarios, removing rain streaks in an unsupervised manner becomes a challenging task, as there are no constraints between images, resulting in suboptimal restoration results. In this paper, we introduce a new unsupervised single-image deraining method called SE-RRACycleGAN, which does not require a paired dataset for training and can effectively leverage the constrained transfer learning capability and cyclic structures inherent in CycleGAN. Since rain removal is closely associated with the analysis of texture features in an input image, we proposed a novel recurrent rain attentive module (RRAM) to enhance rain-related information detection by simultaneously considering both rainy and rain-free images. We also utilize the squeeze-and-excitation enhancement technique to the generator network to effectively capture spatial contextual information among channels. Finally, content loss is introduced to enhance the visual similarity between the input and generated images. Our method excels at removing numerous rain streaks, preserving a smooth background, and closely resembling the ground truth compared to other approaches, based on both quantitative and qualitative results, without the need for paired training images. Extensive experiments on synthetic and real-world datasets demonstrate that our approach shows superiority over most unsupervised state-of-the-art techniques, particularly on the Rain12 dataset (achieving a PSNR of 34.60 and an SSIM of 0.954) and real rainy images (achieving a PSNR of 34.17 and an SSIM of 0.953), and is highly competitive when compared to supervised methods. Moreover, the performance of our model is evaluated using RMSE, FSIM, MAE, and the correlation coefficient, achieving remarkable results that indicate a high degree of accuracy in rain removal and strong preservation of the original image’s structural details. Full article

Cerium-based materials (CeO_2−x) are of significant interest in the development of vacancy-modulated resistive switching (RS) memory devices. However, the influence of grain boundaries on the performance of memristors is very limited. To fill this gap, this study explores the influence of grain boundaries in cerium-based thin film resistive random-access memory (RRAM) devices. Sm_0.2Ce_0.8O_2−x (SDC20) thin films were deposited on (100)-oriented Nb-doped SrTiO₃ (NSTO) and (110)-oriented NSTO substrates using pulsed laser deposition (PLD). Devices constructed with a Pt/SDC20/NSTO structure exhibited reversible and stable bipolar resistive switching (RS) behavior. The differences in conduction mechanisms between single-crystal and polycrystalline devices were confirmed, with single-crystal devices displaying a larger resistance window and higher stability. Combining the results of XPS and I–V curve fitting, it was confirmed that defects near the grain boundaries in the SDC-based memristors capture electrons, thereby affecting the overall performance of the RRAM devices. Full article

In this study, the resistive switching phenomena in TiN/Ti/HfO₂/Ti metal–insulator–metal stacks is investigated, mainly focusing on the analysis of set and reset transitions. The electrical measurements in a wide temperature range reveal that the switching transitions require less voltage (and thus, less energy) as temperature rises, with the reset process being much more temperature sensitive. The main conduction mechanism in both resistance states is Space-charge-limited Conduction, but the high conductivity state also shows Schottky emission, explaining its temperature dependence. Moreover, the temporal evolution of these transitions reveals clear differences between them, as their current transient response is completely different. While the set is sudden, the reset process development is clearly non-linear, closely resembling a sigmoid function. This asymmetry between switching processes is of extreme importance in the manipulation and control of the multi-level characteristics and has clear implications in the possible applications of resistive switching devices in neuromorphic computing. Full article

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A “sandwich” capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays’ benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. Full article

Al alloying/doping in HfO₂-based resistive random-access memory (RRAM) has been proven to be an effective method for improving the low-resistance state (LRS) retention. However, a detailed understanding of Al concentration on oxygen vacancy migration and resistive switching (RS) behaviors still needs to be included. Herein, the impact of Al concentration on the RS properties of the TiN/Ti/HfAlO/TiN RRAM devices is addressed. Firstly, it is found that the forming voltage, SET voltage, and RESET voltage can be regulated by varying the Al doping concentration. Moreover, we have demonstrated that the device with 15% Al shows the minimum cycle-to-cycle variability (CCV) and superior endurance (over 10⁶). According to density-functional theory (DFT) calculations, it is found that the increased operation voltage, improved uniformity, and improved endurance are attributed to the elevated migration barrier of oxygen vacancy through Al doping. In addition, LRS retention characteristics of the TiN/Ti/HfAlO/TiN devices with different Al concentrations are compared. It is observed that the LRS retention is greatly enhanced due to the suppressed lateral diffusion process of oxygen vacancy through Al doping. This study demonstrates that Al alloying/doping greatly affects the RS behaviors of HfO₂-based RRAM and provides a feasible way to improve the RS properties through changing the Al concentration. Full article

In this study, the bipolar switching behaviors in ZnO/HfO₂ bilayer resistive random-access memory (RRAM), depending on different metal top electrodes (TE), are analyzed. For this purpose, devices with two types of TE–TiN/Ti and Pd, which have varying oxygen affinities, are fabricated. X-ray diffraction (XRD) analysis shows that ZnO has a hexagonal wurtzite structure, and HfO₂ exhibits both monoclinic and orthorhombic phases. The average grain sizes are 10.9 nm for ZnO and 1.55 nm for HfO₂. In regards to the electrical characteristics, the I–V curve, cycling test, and voltage stress are measured. The measurement results indicate that devices with TiN/Ti TE exhibit lower set and higher reset voltage and stable bipolar switching behavior. However, a device with Pd TE demonstrates higher set and lower reset voltage. This phenomenon can be explained by the Gibbs free energy of formation (∆G_f°). Additionally, the Pd TE device shows unstable bipolar switching characteristics, where unipolar switching occurs simultaneously during the cycling test. This instability in devices with Pd TE could potentially lead to soft errors in operation. For guaranteeing stable bipolar switching, the oxygen affinity of material for TE should be considered in regards to ZnO/HfO₂ bilayer RRAM. Full article

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. Full article

In this study, a Y₂O₃ insulator was fabricated via the sol–gel process and the effect of precursors and annealing processes on its electrical performance was studied. Yttrium(III) acetate hydrate, yttrium(III) nitrate tetrahydrate, yttrium isopropoxide oxide, and yttrium(III) tris (isopropoxide) were used as precursors, and UV/ozone treatment and high-temperature annealing were performed to obtain Y₂O₃ films from the precursors. The structure and surface morphologies of the films were characterized via grazing-incidence X-ray diffraction and scanning probe microscopy. Chemical component analysis was performed via X-ray spectroscopy. Electrical insulator characteristics were analyzed based on current density versus electrical field data and frequency-dependent dielectric constants. The Y₂O₃ films fabricated using the acetate precursor and subjected to the UV/ozone treatment showed a uniform and flat surface morphology with the lowest number of oxygen vacancy defects and unwanted byproducts. The corresponding fabricated capacitors showed the lowest current density (J_g) value of 10⁻⁸ A/cm² at 1 MV/cm and a stable dielectric constant in a frequency range of 20 Hz–100 KHz. At 20 Hz, the dielectric constant was 12.28, which decreased to 10.5 at 10⁵ Hz. The results indicate that high-quality, high-k insulators can be fabricated for flexible electronics using suitable precursors and the suggested low-temperature fabrication methods. Full article

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. Full article

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 R_off/R_on = 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. Full article

Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged into Crossbar arrays, where rectifying selector devices are required for correct read operation of the memory cells. One of the key advantages of RRAM is its high scalability due to the filamentary mechanism of resistive switching, as the cell conductivity is not dependent on the cell area. Thus, a selector device becomes a limiting factor in Crossbar arrays in terms of scalability, as its area exceeds the minimal possible area of an RRAM cell. We propose a tunnel diode selector, which is self-aligned with an RRAM cell and, thus, occupies the same area. In this study, we address the theoretical and modeling aspects of creating a self-aligned selector with optimal parameters to avoid any deterioration of RRAM cell performance. We investigate the possibilities of using a tunnel diode based on single- and double-layer dielectrics and determine their optimal physical properties to be used in an HfOx-based RRAM Crossbar array. Full article

In the era of widespread edge computing, energy conservation modes like complete power shutdown are crucial for battery-powered devices, but they risk data loss in volatile memory. Energy autonomous systems, relying on ambient energy, face operational challenges due to power losses. Recent advancements in emerging nonvolatile memories (NVMs) like FRAM, RRAM, MRAM, and PCM offer mature solutions to sustain work progress with minimal energy overhead during outages. This paper thoroughly reviews utilizing emerging NVMs in microcontroller units (MCUs), comparing their key attributes to describe unique benefits and potential applications. Furthermore, we discuss the intricate details of NVM circuit design and NVM-driven compute-in-memory (CIM) architectures. In summary, integrating emerging NVMs into MCUs showcases promising prospects for next-generation applications such as Internet of Things and neural networks. Full article

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. Full article

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. Full article