Read Voltage Research Articles

Renewable Power Energy Management for Single and Three-phase Inverters Design

This study manages solar panels, wind turbines, and fuel cells to develop single- and three-phase Sinusoidal Pulse Width Modulation (SPWM) inverter circuits. The maximum power point tracking method in direct current (DC)-DC boost converters efficiently uses these renewable sources. Photovoltaic (PV), wind turbines, fuel cells, and bidirectional batteries are utilized in energy management. Efficiency, material cost reduction, and user convenience depend on energy management. The technology fully charges the standby battery when all sources are available. Other renewable sources will power the system if one fails. Before boost converter circuits, renewable energies generated 310V for PV, 60V for wind energy conversion, 215V for fuel cells, and 25.9V for bi-directional batteries. Proportional-integral (PI) controllers govern inverter switching to maintain high-quality sinusoidal outputs while decreasing total harmonic distribution (THD). Single-phase load voltage and current at resistive load have 0.65419% THD. Load voltage and current root main square (RMS) values are 217.3617 (V) and 3.1052 (A), respectively. When connected to an R load, the three-phase inverter circuit provides 1.5146% THD for load voltage and current. Load voltage and current RMS readings are 218.4261 (V) and 2.1843 (A). THD is 1.5969% for load voltage and 1.2905% for load current with an RL load. In this example, load voltage and current RMS values are 214.201 (V) and 2.0916 (A). Simulations demonstrate the successful integration of renewable sources to meet energy needs and maintain power supply. The above solutions use renewable energy efficiently and integrate with the grid.

Compensated Current Mirror Neuron Circuits for Linear Charge Integration with Ultralow Static Power in Spiking Neural Networks

For energy‐ and time‐efficient artificial intelligence (AI) computing, implementing hardware‐based spiking neural networks (SNNs) has become a core technology. In SNNs, synaptic devices store weights in memory, and neurons process received weighted information and generate spike signals. Upon feeding spike signals into synaptic arrays, the synaptic weights multiply the signals, which subsequently sum up to perform vector‐matrix multiplication (VMM). Simultaneous access to multiple synaptic devices, however, reduces the equivalent resistance of these synaptic arrays. This reduction alters the voltage division between the pre‐synaptic array and the input resistance of the neuron circuit, distorting the read voltage across synaptic devices. This phenomenon is known as the fan‐in problem, which leads to non‐ideal VMM operations and degrades system accuracy. To address this issue, a novel compensated current mirror (CCM) neuron circuit is proposed, which incorporates a single additional transistor into a conventional current mirror. This CCM neuron achieves exceptional current linearity (R2 > 0.999) and efficiently compensates for VMM error with low complexity and energy consumption (3.33 pJ spike−1). Furthermore, the CCM neuron demonstrates ≈7‐%p higher inference accuracy than conventional ones when integrated with a 512 × 512 large‐scale synaptic array, which is comparable to the accuracy of software‐based SNNs.

3D Nano Hafnium‐Based Ferroelectric Memory Vertical Array for High‐Density and High‐Reliability Logic‐In‐Memory Application

AbstractA new type of ferroelectric memory device with high reliability and complementary metal‐oxide‐semiconductor (CMOS) compatibility characteristics is an important condition for achieving integrated memory and computing chips. Here, 3D stacked ferroelectric memory devices based on ferroelectric materials of HfO2 are fabricated. The device exhibits high speed (50 ns), low read voltage (0.5 V), and great reliability with no substantial degradation after 1010 cycles and a 10‐years data retention at 85 °C. The IMP and NAND logic are achieved with stable memory window (>200 mV) across the vertical devices’ interconnection. On this basis, combining with the traditional CMOS logic device, multiple combination logic functions containing NOT, AND, and NOR are achieved by simulation. The collaboration of devices in the vertical direction providing the possibility of combining multi‐bit logic in memory functions and paves the way for the implementation of high‐density, high‐reliability, and low‐energy consumption computing‐in‐memory chips compatible with the CMOS technology.

Coupled Electro-Thermal-Aging Battery Pack Modeling—Part 1: Cell Level

This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something current models often lack but is essential for system level simulations, relevant for electric vehicle manufacturers. The aging analysis includes both cycling and calendar effects across the lifetime of the cell and reversible and irreversible heat in a lumped-mass model to capture the temperature evolution of the cell in operation. The Thévenin equivalent circuit model with capacitance used to simulate the electrical behavior of the cell was experimentally validated, showing a high correlation with the proposed model during the charging and discharging phases. A maximum error of 3% on the voltage reading was identified during discharge with the complete model. This model was also designed to be used as a stepping stone for a comprehensive model at the module and vehicle levels that can later be used by designers.

Enhancing Uniformity, Read Voltage Margin, and Retention in Three-Dimensional and Self-Rectifying Vertical Pt/Ta2O5/Al2O3/TiN Memristors.

This study introduces a Ta2O5-based self-rectifying memristor (SRM) with an Al2O3 interfacial layer adopted to improve switching uniformity, read voltage margin, and long-term retention. The Pt/Ta2O5/Al2O3/TiN (PTAT) device exhibits a 105 rectification ratio, 104 on/off ratio, 2 × 106 endurance, and retention of 104 s at 150 °C. A 3-layer 4 × 4 vertical resistive random access memory structure exhibits uniform switching parameters. The coefficient of variation (CV) for device-to-device measurements is 0.23 for the low resistance state (LRS) and 0.22 for the high resistance state (HRS), while for cycle-to-cycle measurements, the CV is 0.38 for the LRS and 0.11 for the HRS. Finally, the present study demonstrates the superior performance of the PTAT devices in the context of hardware-aware training for a fully connected neural network implementation. These advancements position the PTAT device as a promising candidate for high-density three-dimensional storage class memory and low-power neural networks, offering the consistent performance and reliability necessary for future high-density storage applications.

Mimicking somatic behavior of neurons using the integrate and fire model of 2D SnS memristive switching characteristics

This Letter presents the fabrication and characterization of a 2D SnS memristor, proposing its integrate and fire (I&F) model as a potential hardware implementation of neuronal somatic behavior. The memristor comprises a thin layer of tin (II) sulfide (SnS) sandwiched between copper (Cu) electrodes on a silicon (Si) substrate. This structure exhibits an impressive Roff:Ron ratio of 103 at a read voltage Vrd of 0.25 V with exceptionally low switching Vsw and set Vset voltages of 0.3 and 0.35 V, respectively, with ∼3 order variation between the maximum Rmax and Rmin resistances offered during single voltage sweep cycle. We have explained the memristive behavior using the dual ionic conduction mechanism in the SnS active layer. We extracted the real-time band diagram of SnS using ultraviolet photoelectron spectroscopy, explaining the low Vsw observed. We propose that the emulation of the I&F artificial neuron model exhibited by the fabricated device could serve as a promising application in the field of artificial neuron spiking.

Heteroepitaxy of ε‐Ga2O3 thin film for artificial synaptic device

AbstractEmerging‐wide bandgap semiconductor Ga2O3 shows distinct characteristics for optoelectronic applications and a stable crystal phase of Ga2O3 is highly desired. Herein, we have first reported a metal‐semiconductor‐metal structure photonic synaptic device based on the ε‐Ga2O3 thin film. The ε‐Ga2O3 epilayer is grown on the c‐sapphire with a low temperature nucleation layer, which presents a crystal orientation relationship with the c‐sapphire (ε‐Ga2O3 <010> // c‐sapphire <1–100> and ε‐Ga2O3 <001> // c‐sapphire <0001>). The ε‐Ga2O3 photonic device was stimulated by UV pulses at different pulse widths, pulse intervals, and reading voltages. Under the UV pulse excitation, the photonic device exhibits primary synaptic functions including excitatory postsynaptic current, short term memory, pair pulse facilitation, long term memory, and STM‐to‐LTM conversion. In addition, stronger and repeated stimuli can naturally contribute to the higher learning capability, thus prolonging the memory time.

ZnO-based artificial synaptic diodes with zero-read voltage for neural network computing

Brain-inspired neuromorphic sensory devices play a crucial role in addressing the limitations of von Neumann systems in contemporary computing. Currently, synaptic devices rely on memristors and thin-film transistors, requiring the establishment of a read voltage. A built-in electric field exists within the p–n junction, enabling the operation of zero-read-voltage synaptic devices. In this study, we propose an artificial synapse utilizing a ZnO diode. Typical rectification curves characterize the formation of ZnO diodes. ZnO diodes demonstrate distinct synaptic properties, including paired-pulse facilitation, paired-pulse depression, long-term potentiation, and long-term depression modulations, with a read voltage of 0 V. An artificial neural network is constructed to simulate recognition tasks using MNIST and Fashion-MNIST databases, achieving test accuracy values of 92.36% and 76.71%, respectively. This research will pave the way for advancing zero-read-voltage artificial synaptic diodes for neural network computing.

Estimation of methyl orange dye’s molar absorptivity using a photoresistor-based photometer

Abstract The need to further develop solar cell technology, particularly on dye-sensitized solar cells (DSSCs), drives absorption studies of various chemical species. In this study, absorbance analysis of methyl orange (MO) dye was performed using the adapted and modified photoresistor-based photometer of Adams-McNichol et al. [1]. The research aims to improve the stability of the reference setup, while maintaining the accuracy of absorbance results it yields. The methodology includes photometer fabrication, MO dye sample preparation, and the evaluation of MO dye’s molar absorptivity in an aqueous solution. Results show that the estimated molar absorptivity of MO using the voltage readings from the photometer is close to the literature value, with a percentage error of 1.44%. This finding demonstrates that the modified photometer retains its effectiveness, as supported by the method repeatability measurements from six samples for each of the following MO concentrations: 0 μM, 40 μM, 45 μM, and 50 μM. Considering these results, the device may be useful in teaching light absorption to students as an alternative to the “black box” approach, and in exploring purified natural dyes that have potential applications in solar cell technology.

Determination of Imprint Effects in Ferroelectrics from the Quantified Phase and Amplitude Response.

Piezoresponse force microscopy (PFM) is a robust characterization technique to explore ferroelectric properties at the nanoscale. However, the PFM signal can lead to misinterpretation of results due to the dominant electrostatic interaction between the tip and the sample. In this work, a detailed calibration process is presented and a procedure to identify the parasitic phase offset is demonstrated. To obtain artifact-free phase-amplitude loops, a methodology is developed by combining the outcomes from switching spectroscopy-PFM (SS-PFM) and Kelvin probe force microscopy (KPFM). It is demonstrated that the phase and amplitude loops obtained from SS-PFM at a specific read voltage, ascertained from the surface potential by KPFM, can convey accurate electromechanical information. These methodologies are applied to quantify the imprint voltage in BaTiO3 and BiFeO3, along with vertically aligned BaTiO3:Sm2O3 and BaTiO3:MgO nanocomposites. The variation of the imprint voltage measured under different tip voltages demonstrates the importance of selecting the correct read voltage in determining the local imprint voltage. Additionally, 2D imprint voltage maps in each domain of a BaTiO3 single crystal are obtained using the datacube-PFM technique, which allows pixel-by-pixel determination of artifact-free spatial variation of PFM phase-amplitude response.

Transformation of MoSe2 to MoSe2-xOyvia controlled oxidation for high-performance resistive switching

The current study shows an improvised resistive switching in oxygenated MoSe2 (MoSe2-xOy) thin film. 2D nanosheets assembled nanoflower morphology of MoSe2 was synthesized with hydrothermal route and then treated with thermal oxidation process at 300⁰C in presence of 0, 0.5, 5 and 20 % of oxygen. The coexistence of MoSe2 and MoOx in each samples was investigated with various spectroscopic techniques. The conversion to MoOx was maximum in 20 % O2 treated sample while other samples were converted partially. Au/MoSe2-xOy/Au structured devices were fabricated on SiO2/p-Si substrate and tested for resistive switching study. Among all four devices, 5 % O2 treated MoSe2-xOy exhibited excellent filamentary type bipolar resistive switching with very low SET and RESET voltages of + 0.84 V and −0.79 V, respectively. The device showed ION/IOFF ratio ∼50 (at read voltage: 0.5 V) with excellent retention (106 s) and endurance (100 cycles) characteristics with minimum cycle-to-cycle variation. The resistive switching mechanism was explained with valance change conducting filament formation/rupture model and the trap-controlled space charge limited current behavior. Rich transport properties of 2D-chalcogenide (MoSe2) and classic oxygen vacancy migrated switching of metal oxide (i.e. MoOx), were simultaneously important for achieving high performance memristive behavior in 5 % O2 treated MoSe2.

Bipolar resistive switching behavior of PVA/g-C3N4 quantum dots hybrid thin films

Graphite-phase carbon nitride quantum dots (g-C3N4 QDs) have garnered widespread attention and considerable research interest as active media for resistive random-access memory (RRAM) devices owing to their exceptional physical properties. In this study, g-C3N4 QDs were synthesized via ultrasonic-assisted liquid-phase exfoliation. Subsequently, the obtained g-C3N4 QDs with uniform size were embedded into polyvinyl alcohol (PVA) for preparing PVA/g-C3N4 QDs hybrid, and the RRAM devices with Al/g-C3N4 QDs-PVA/ITO/PET structure were fabricated. The obtained memory devices exhibit bipolar resistive switching (BRS) behavior and are primarily influenced by the space charge limited conduction (SCLC) mechanism. The data retention time exceeds 104 s at a read voltage of 0.1 V, accompanied by an impressively low set voltage. In addition, the device exhibits a surprising temperature dependence of the resistive switching performance at different temperatures. The aforementioned properties demonstrate the significant potential of g-C3N4 QDs in RRAM for data storage applications.

Designing a Real-Time DataLogger Using the Internet of Things for a Basic Imple Home Kwh Meter Capable of 1300 VA

Researchers want to make a kWh meter monitoring tool with estimated usage costs so that the tools made can help people to minimize costs in electricity usage. The purpose of this research is the creation of an IoT-based kWh meter monitoring tool that has the ability to read current, voltage, frequency, power factor and power usage that flows and shows the use of costs that have been used in real time with the display results on the LCD and also on the user's smartphone. In the reading results of usage costs (Rp) on the tool and PLN there is also a difference of Rp. 12,000, where the reading results on the tool are also lower than the results of the payment slip provided by PLN. The cost reading error rate on the tool is 4.4% with a reading accuracy rate of 95.6%.

Light-Adapted Optoelectronic-Memristive Device for the Artificial Visual System.

With the development of artificial intelligence systems, it is necessary to develop optoelectronic devices with photoresponse and storage capacity to simulate human visual perception systems. The key to an artificial visual perception system is to integrate components with both sensing and storage capabilities of illumination information. Although module integration components have made useful progress, they still face challenges such as multispectral response and high energy consumption. Here, we developed a light-adapted optoelectronic-memristive device integrated by an organic photodetector and ferroelectric-based memristor to simulate human visual perception. ITO/P3HT:PC71BM/Au as the light sensor unit shows a high on/off ratio (Iph/Id) reaching ∼5 × 104 at 0 V. The memristor unit, consisting of ITO/CBI@P(VDF-TrFE)/Cu, has a RON/ROFF ratio window of ∼106 under 0.05 V read voltage and ultralow power consumption of ∼1 pW. Moreover, the artificial visual perception unit shows stable light-adapted memory windows under different wavelengths of irradiation light (400, 500, and 600 nm; they meet the spectral range of human visual recognition) and can clearly identify the target image ("T" shape) because of the apparent contrast, which results from the high ROFF/RON ratio values. These results provide a potential design strategy for the development of intelligent artificial vision systems.

A Reconfigurable Ferroelectric Transistor as An Ultra‐Scaled Cell for Low‐Power In‐Memory Data Processing

AbstractCompact in‐memory computing architectures are desirable to embed artificial intelligence (AI) in resource‐restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field‐effect transistor (ferro‐TFET) is presented that can be used as an ultra‐scaled cell for low‐power in‐memory data processing. A gate‐all‐around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non‐volatilely reconfigurable anti‐ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single‐gate ferro‐TFET. It is shown that the ferro‐TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse‐response method considering the effect of device‐to‐device variation based on statistics. Benefiting from the miniaturized footprint, non‐volatility, and low‐power operation, ferro‐TFETs show promises as a one‐transistor in‐memory computing cell for area‐ and energy‐efficient edge AI applications.

Sistem Manajemen Baterai Pada Peralatan Catu Daya Di Equipment Room Stasiun Manggarai Dengan Aplikasi Blynk Berbasis Esp8266

This research aims to develop an efficient battery monitoring system using the ESP8266 module and INA219 sensor. Monitoring the battery condition effectively is crucial in various applications, especially in portable systems or isolated systems where real-time supervision is required. The method used in this research is the utilization of the ESP8266 module as the main microcontroller connected to the INA219 sensor to monitor the battery voltage, current, and power in real-time. The data obtained by the INA219 sensor is transmitted through the WiFi network managed by ESP8266 to a server or monitoring platform accessible to users via Blynk a web-based application or mobile application. The test results show that the system is capable of providing accurate information about the battery condition, including the charge level, usage current, and potential issues such as overcharge or undercharge. The implementation of this system is expected to be applicable in various applications, including renewable energy systems, portable devices, and electric vehicles, to improve battery performance and safety. The INA219 module as a voltage and current sensor found a voltage reading error percentage with a range of 0%-0.66% and an average of 0.18%. For readings at current with an error range of 0.91%-7.57%. For internal resistance readings which are calculated based on dividing the clamp voltage value by the circuit current, an error range of 0.04% -30.36% is found.

IMPROVING APPROACHES TO DETERMINING THE LEVEL OF FILLING OF THE PIPE BODY WITH PUMPED PRODUCT

As of 2015, there were more than 200 permanent and variable gravity sections of crude oil and petroleum product pipelines in the pipeline system of PJSC Transneft, the natural monopoly for pipeline transportation of crude oil and petroleum products in the Russian Federation. The operation of gravity sections in oil pipelines in Russia is a very important safety and environmental issue, and Russian oil transportation companies are constantly working on improving approaches to monitor and control pipeline operation modes. When these sections occur, special attention is given to determining the geometric capacity of the pipeline section, as pumping is done in batches and commercial pumping product remains in the gravity section during periodic shutdowns. Due to the limited technical equipment of these sections and the likely illegal unrecorded product withdrawals in these pipeline sections, the most demanding approach is to determine the fill factor of the pipe body. The fill factor is influenced by many factors, including the presence of gas phase and water accumulations, correct geometry of the pipe body and other factors. Given the given initial data, it can be seen that the solution to the problem of determining the filling level of a stopped oil pipeline is reduced to the construction of a multifactor model. For accurate determination, in addition to the model, there is a need for relevant software, an algorithm for solving the problem ofcalculating the filling factor. Correspondingly, operating organisations need a system for accounting and control of the product filling level at these sections at the level of improvement of the state system for ensuring unity of measurements and metrological support of accounting for the long-distance pipeline transport of oil and oil products - a strategic resource, the revenues from the sale of which account for about 23 % of the budgetary system of the Russian Federation.The calculation created and proposed by the authors is divided into stages in which the initial data is sequentially collected, the voltages on the capacitors are calculated, the data is processed using the interpolation method, an image is constructed using the Newton-Gauss method, and the filling factor is calculated with various multiphase pumping products. The calculation is made based on voltage readings from sensors located orthogonally to the axis of the pipe body. Using the approximation method and the Newton-Gauss method, you can get rid of the «noise» and level the phase boundary. When tested and implemented at pipeline pumping facilities, it will be possible to clarify the existing dependences of the influence of gas phase friction on the fluid flow rate, and, therefore, evaluate the effect on the filling factor. This calculation can become a tool in the fight against illegal tappings.

Sol-Gel Derived ZnO Thin Films with Nonvolatile Resistive Switching Behavior for Future Memory Applications

Herein we report on a facile sol-gel spin-coating technique to fabricate ZnO thin films that grow preferentially along the (002) plane on FTO substrates. By employing the magnetron sputtering technique to deposit a tungsten (W) top metal electrode onto these ZnO thin films, we successfully realize a W/ZnO/FTO memory device that exhibits self-rectifying and forming-free resistive switching characteristics. Notably, the as-prepared device demonstrates impressive nonvolatile and bipolar resistive switching behavior, with a high resistance ratio (RHRS/RLRS) exceeding two orders of magnitude at a reading voltage of 0.1 V. Moreover, it exhibits ultralow set and reset voltages of approximately +0.5 V and −1 V, respectively, along with exceptional durability. In terms of carrier transport properties, the low resistance state of the device is dominated by ohmic conduction, whereas the high resistance state is characterized by trap-controlled space-charge-limited current conduction. This work highlights the potential of the ZnO-based W/ZnO/FTO memory device as a promising candidate for future high-density nonvolatile memory applications.

A Passive Approach to Mine Detection and Classification Utilizing Magnetic Fields and Convolutional Neural Networks

Active detectors are widely used today for mine detection, providing high accuracy. However, their operating principle can pose a danger, as it has the potential to trigger the detonation of hidden mines. This study introduces an innovative method for detecting and classifying mines composed of different materials (metallic, semi-metallic, plastic) through the use of a convolutional neural network. The network analyzes anomalies derived from a uniquely designed feature matrix, achieving notable accuracy in classification. The classification process is based on a feature input vector that includes: voltage readings from the FLC-100 magnetic field sensor, which detects magnetic field anomalies near mines with sensitivities ranging from 10⁻¹⁰ to 10⁻⁴ Tesla; data from six soil types, considering their moisture content; and the sensor's height above the mine. Since mines made from different materials exhibit distinct magnetic properties, utilizing magnetic field anomaly data allows for effective material-based mine classification. The classification accuracy was evaluated using various metrics, such as F1, Precision, Accuracy, FPR, FNR, Recall, and TNR.

Exploring Disturb Characteristics in 2D and 3D Ferroelectric NAND Memory Arrays for Next-Generation Memory Technology.

Ferroelectric transistors are considered promising for next-generation 3D NAND technology due to their lower power consumption and faster operation compared to conventional charge-trap flash memories. However, ensuring their suitability for such applications requires a thorough investigation of array-scale reliability. This study specifically examines the suitability of hafnia-based ferroelectric transistors for advanced 3D NAND applications, with a specific focus on establishing a disturb-free voltage scheme to ensure the reliability of ferroelectric transistors within the array. Our key finding highlights the crucial role of optimal pass voltage in achieving disturb-free operation in both 2D and 3D ferroelectric NAND arrays. Additionally, the study indicates that read disturb remains negligible when an appropriate read voltage is applied. These insights provide a practical strategy for achieving reliable operation in 2D and 3D ferroelectric NAND, highlighting the potential of hafnia-based ferroelectric materials to meet the evolving requirements of high-density and reliable NAND flash memory applications.