GCME- Artículos de revista

Reset transition in HfO2-Based memristors using a constant power signal

2025-01-01T00:00:00Z

Memristors, also known as resistive switching devices, have great potential for applications in memory and neuromorphic systems. Understanding the switching mechanisms is crucial since ReRAM memories need to operate at high frequencies. It is known the reset transition is dominated by the conductive filament Joule heating. We have studied the reset transition in TiN/Ti/HfO2/W metal–insulator–metal memristors by applying constant power signals to different initial filament thicknesses, which were obtained using different initial low resistance states. The results show that power value controls the reset times, decreasing when the power is increased. On the other hand, larger resistances lead to faster reset transitions. These measurements have allowed us to obtain a value of the thermal resistance of the conductive filament. Moreover, we have observed the reset times as a function of the initial resistance and the power lies on a common plane, which allows us to estimate the transition time by fixing an initial resistance and a power value.

Multilevel conductance modulation in HfO2, Al2O3, and HfO2/Al2O3 bilayer memristors

2026-01-01T00:00:00Z

Memristors have drawn interest due to their use as artificial synapses in neuromorphic circuits. This work investigates the multilevel conductance modulation in Al2O3 and HfO2-based memristors. Specifically, the control of the depression or reset transition when applying identical consecutive voltage pulses was the main objective. Both pulse amplitude and pulse accumulated time can control the reset transition. Voltage required to reset the device is higher for Al2O3, which can lead to higher energy consumption. However, this material showed better reset transition linearity.

Effect of set and reset dynamics on HfO2, Al2O3, and bilayer memristors

2025-01-01T00:00:00Z

In this study, resistive switching in three structures with HfO, AlO, and bilayer (HfO + AlO) oxides is studied. Electrical characterization reveals differences in switching dynamics and performance across these configurations, highlighting the impact of oxide composition and structure on device behavior. The time needed to reset is defined and studied in detail, showing an exponential dependence with the applied voltage. Finally, an initial assessment of the effect that the set and reset transient has on the multilevel capabilities of the devices is made.

Dynamics of set and reset processes in HfO2 -based bipolar resistive switching devices

2025-01-01T00:00:00Z

The temporal evolution of the set and reset processes in TiN/Ti/HfO2/W metal-insulator-metal devices exhibiting resistive switching behavior is investigated in depth. To this end, current transients were recorded by applying different voltages, which allowed us to change the conductance of the device. While both set and reset transitions are faster with increasing applied voltage, they clearly exhibit different time responses. The set transition is characterized by a monotonic increase in current after a sudden initial rise in its value, while the reset transition is characterized by a notably nonlinear response that resembles a sigmoidal function. We have successfully modeled the reset current transient with a bi-dose function and defined its time constant (Time-to-Reset) as the time where the current variation reaches its maximum value. Our findings show that varying the initial conditions of the reset process, such as increasing the temperature and/or decreasing the initial resistance value, significantly affect the reset transient, exponentially increasing the reset time constant value. This allows us to model its dependencies with the equation of a plane.

Electrical and magnetic properties of atomic layer deposited cobalt oxide and iron oxide stacks

2025-01-01T00:00:00Z

Cobalt and iron oxides, due to their tunable structural and magnetic properties, are widely studied for electronic and spintronic applications. However, achieving high coercivity and saturation magnetization in ultrathin films remains a challenge. In this work, we report on the atomic layer deposition (ALD) of nanolaminates and mixed cobalt–iron oxide films on silicon and TiN substrates at 300–450 °C. Using supercycle and multistep ALD methods with ferrocene and cobalt acetylacetonate precursors, we synthesized Co3O4–Fe2O3 bilayers and ternary ferrites (Co2FeO4 and CoFe2O4). The structural, morphological, electrical, and magnetic properties were characterized. We observed that thin films (∼7–12 nm) exhibit markedly enhanced breakdown fields and exceptional magnetic coercivity (up to 25 kOe) and saturation magnetization (up to 1000 emu cm−3), especially after annealing. These results demonstrate a viable route to engineer ferrite-based thin films with superior magnetic and dielectric performance at nanoscale thicknesses.

Data augmentation in predictive maintenance applicable to hydrogen combustion engines: a review

2024-01-01T00:00:00Z

Machine-learning-based predictive maintenance models, i.e. models that predict break- downs of machines based on condition information, have a high potential to minimize maintenance costs in industrial applications by determining the best possible time to per- form maintenance. Modern machines have sensors that can collect all relevant data of the operating condition and for legacy machines which are still widely used in the industry, retrofit sensors are readily, easily and inexpensively available. With the help of this data it is possible to train such a predictive maintenance model. The main problem is that most data is obtained from normal operating conditions, whereas only limited data are from failures. This leads to highly unbalanced data sets, which makes it very difficult, if not impossible, to train a predictive maintenance model that can detect faults reliably and timely. Another issue is the lack of available real data due to privacy concerns. To address these problems, a suitable data generation strategy is needed. In this work, a litera- ture review is conducted to identify a solution approach for a suitable data augmentation strategy that can be applied to our specific use case of hydrogen combustion engines in the automotive field. This literature review shows that, among the different state-of-the-art proposals, the most promising for the generation of reliable synthetic data are the ones based on generative models. The analysis of the different metrics used in the state of the art allows to identify the most suitable ones to evaluate the quality of generated signals. Finally, an open problem in research in this area is identified and it is the need to validate the plausibility of the data generated. The generation of results in this area will contribute decisively to the development of predictive maintenance models.

Thermal dependence of the current in TiN/Ti/HfO2/W memristors at different intermediate conduction states

2014-01-01T00:00:00Z

The dependence of the current in TiN/Ti/HfO2/W devices on the temperature is investigated in the range from 78 K to 340 K. Resistive switching cycles at 78 K are conducted to explore the thermal dependence in filament configurations with different intermediate resistance states. The less conductive states show an increase of the current as the temperature rises, while the fully formed filament displays a metallic-like behavior. A comprehensive model, based on the Stanford Model including a series resistance, is proposed and successfully validated by experimental data. The interplay between the ohmic and non-linear components in the model for different filament states is analyzed, emphasizing the dominance of the non-linear component (and its corresponding thermal dependence) in partially formed filaments and the prevalence of the ohmic component in the fully formed filament, which shows a decreasing current as the temperature rises. A complete compact model for simulation of circuits including the thermal dependence of these devices is developed.

3DWS: reliable segmentation on intelligent welding systems with 3D convolutions

2023-01-01T00:00:00Z

Automated industrial welding processes depend on a large number of factors interacting with high complexity resulting in some sporadic and random variability of the manufactured product that may affect its quality. It is therefore very important to have an accurate and stable quality control. In this work, a deep learning (DL) model is developed for semantic segmentation of weld seams using 3D stereo images of the seam. The objective is to correctly identify the shape and volume of the weld seam as this is the basic problem of quality control. To achieve this, a model called UNet++ has been developed, based on the UNet and UNet++ architectures, with a more complex topology and a simple encoder to achieve a good adaptation to the specific characteristics of the 3D data. The proposed model receives as input a voxelized 3D point cloud of the freshly welded part where noise is abundantly visible, and generates as output another 3D voxel grid where each voxel is semantically labeled. The experiments performed with parts built by a real weld line show a correct identification of the weld seams, obtaining values between 0.935 and 0.941 for the Dice Similarity Coefficient (DSC). As far as the authors are aware, this is the first 3D analysis proposal capable of generating shape and volume information of weld seams with almost perfect noise filtering.

Interpretability of deep learning models in analysis of Spanish financial text

2024-01-01T00:00:00Z

Artificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way. For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008–2013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means.

Standards for the Characterization of Endurance in Resistive Switching Devices

2021-01-01T00:00:00Z

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Off-chip prefetching based on Hidden Markov Model for non-volatile memory architectures

2021-01-01T00:00:00Z

Non-volatile memory technology is now available in commodity hardware. This technology can be used as a backup memory for an external dram cache memory without needing to modify the software. However, the higher read and write latencies of non-volatile memory may exacerbate the memory wall problem. In this work we present a novel off-chip prefetch technique based on a Hidden Markov Model that specifically deals with the latency problem caused by complexity of off-chip memory access patterns. Firstly, we present a thorough analysis of off-chip memory access patterns to identify its complexity in multicore processors. Based on this study, we propose a prefetching module located in the llc which uses two small tables, and where the computational complexity of which is linear with the number of computing threads. Our Markov-based technique is able to keep track and make clustering of several simultaneous groups of memory accesses coming from multiple simultaneous threads in a multicore processor. It can quickly identify complex address groups and trigger prefetch with very high accuracy. Our simulations show an improvement of up to 76% in the hit ratio of an off-chip dram cache for multicore architecture over the conventional prefetch technique (g/dc). Also, the overhead of prefetch requests (failed prefetches) is reduced by 48% in single core simulations and by 83% in multicore simulations.

The asset administration shell as enabler for predictive maintenance: a review

2023-01-01T00:00:00Z

The emergence of the Internet of Things and the interconnection of systems and machines enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction and communication between physical and digital entities, leading to the creation of cyber-physical systems. The digital twin and the standard for the Asset Administration Shell are concepts derived from Industry 4.0 that exploit the advantages of connecting the physical and virtual domains, improving the management and display of the collected data. Furthermore, the increasing availability of data has enabled the implementation of data-driven approaches, such as machine and deep learning models, for predictive maintenance in industrial and automotive applications. This paper provides a two-dimensional review of the Asset Administration Shell and data-driven methods for predictive maintenance, including fault diagnosis and prognostics. Additionally, a digital twin architecture combining the Asset Administration Shell, predictive maintenance and data-driven methods is proposed within the context of the WaVe project.

Prediction of the sleep apnea severity using 2D-convolutional Neural Networks and respiratory effort signals

2023-01-01T00:00:00Z

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC)=0.75 and a root mean square error (RMSE)=10.33 events/h when estimating the apnea-hypopnea index, and ICC=0.83 and RMSE=0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

Inhomogeneous HfO2 layer growth at atomic layer deposition

2023-01-01T00:00:00Z

Thin HfO2 films atomic layer deposited from hafnium alkyl amide and oxygen plasma were analysed using spectroscopic ellipsometry and X-ray reflectivity. Low refractive index of the material for samples with less than 30 nm thickness marks the index inhomogeneity at the first stage of growth. The transition from rising density to a more stable growth takes place at about 10 to 25 nm film thickness. HfO2 films used for resistive switching experiments demonstrate either clockwise or counterclockwise behaviour depending on the film thickness. The reason for this may be the disruption of the conductive filament at different metal-insulator interfaces, which could be favoured by several mechanisms.

Empirical Characterization of ReRAM Devices Using Memory Maps and a Dynamic Route Map

2022-01-01T00:00:00Z

Memristors were proposed in the early 1970s by Leon Chua as a new electrical element linking charge to flux. Since that first introduction, these devices have positioned themselves to be considered as possible fundamental ones for the generations of electronic devices to come. In this paper, we propose a new way to investigate the effects of the electrical variables on the memristance of a device, and we successfully apply this technique to model the behavior of a TiN/Ti/HfO2/W ReRAM structure. To do so, we initially apply the Dynamic Route Map technique in the general case to obtain an approximation to the differential equation that determines the behaviour of the device. This is performed by choosing a variable of interest and observing the evolution of its own temporal derivative versus both its value and the applied voltage. Then, according to this technique, it is possible to obtain an approach to the governing equations with no need to make any assumption about the underlying physical mechanisms, by fitting a function to this. We have used a polynomial function, which allows accurate reproduction of the observed electrical behavior of the measured devices, by integrating the resulting differential equation system.

An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices

2022-01-01T00:00:00Z

An in-depth simulation and experimental study has been performed to analyze thermal effects on the variability of resistive memories. Kinetic Monte Carlo (kMC) simulations, that reproduce well the nonlinearity and stochasticity of resistive switching devices, have been employed to explain the experimental results. The series resistance and the transition voltages and currents have been extracted from devices based on the TiN/Ti/HfO2/W stack we have fabricated and measured at temperatures ranging from 77 K to 350 K. We observed that the variability for all the magnitudes analyzed was much higher at low temperatures. In the kMC simulations, we obtained conductive filaments (CFs) with less compactness at low temperatures. This led us to explain the higher variability, based on the variations of the CF morphology and density seen at low temperatures.