Design Optimization Of Devices Research Articles

Advanced characterization of SiC devices by optical beam induced current (OBIC): Experimental and simulation results

Wide-bandgap semiconductors, such as silicon carbide (SiC), gallium nitride (GaN), and diamond (C), have proven to outperform traditional silicon (Si) based power electronic devices. However, the peripheral protection of these wide-bandgap devices requires careful design to handle high voltage effectively. This article highlights the potential of using the Optical Beam Induced Current (OBIC) technique to analyze the effectiveness of different protection methods and provide valuable feedback to device designers regarding peripheral termination efficiency. Designers need simulations representative of the measurement, In this article a finite element simulation is used to determine the induced current as a function of the position of the optical beam.Firstly a theoretical approach will be presented to introduce the OBIC method. Subsequently, the electro-optical characterization technique is applied to a PiN diode protected by narrow field rings.The article also showcases new results obtained from components with new-generation peripheral protection, demonstrating the continuous progress in this field.Overall, this article highlights the significance of the OBIC technique in evaluating the effectiveness of peripheral protection methods for wide-bandgap semiconductors, particularly SiC devices. By providing valuable insights into the electric field's spatial distribution, this technique aids in optimizing device design and improving overall performance. Finite element simulations in relation to the characteristics of the UV beam and its scanning across the surface of the component help to assess the geometric parameter impact on the electrical results.

Boosting External Quantum Efficiency of Blue Perovskite QLEDs Exceeding 23% by Trifluoroacetate Passivation and Mixed Hole Transportation Design.

Perovskite quantum dot-based light-emitting diodes (QLEDs) have been considered a promising display technology due to their wide color gamut for authentic color expression. Currently, the external quantum efficiency (EQE) for state-of-the-art blue perovskite QLEDs is about 15%, which still lags behind its green and red counterparts (>25%) and blue film-based LEDs. Here, blue perovskite QLEDs that achieve an EQE of 23.5% at 490nm is presented, to the best knowledge, which is the highest value reported among blue perovskite-based LED fields. This impressive efficiency is achieved through a combination of quantum dot (QD) passivation and optimal device design. First, blue mixed halide perovskite CsPbCl3- xBrx QDs passivated by trifluoroacetate exhibit excellent exciton recombination behavior with a photoluminescence quantum yield of 84% due to reducing uncoordinated Pb surface defects. Furthermore, the device is designed by introducing a mixed hole-transport layer (M-HTL) to increase hole injection and transportation capacity and improve carrier balance. It is further found that M-HTL can decrease carrier leakage and increase radiative recombination in the device, evidenced by the visual electroluminescence spectrum at 2.0V. The work breaks through the EQE gap of 20% for blue perovskite-based QLEDs and significantly promotes their commercialization process.

Design and hydrodynamic study of a new pile-based breakwater-OWC device combined system

In order to enhance the conversion efficiency and optimize the hydrodynamic performance of the OWC device, a novel combined system composed of a pile-based OWC chamber and a perforated wave-eliminator is proposed. The schemes with rectangular front wall M1, elliptical front wall M2, M3 are designed. Physical tests and numerical simulations were conducted for the hydrodynamic process under the action of wave of the system with H/d = 0.16, 0.20, 0.24 and L/B = 3.3, 3.8, 4.3, 4.7, 5.2, 5.5, 6.4. The obtained-results show that the system has better energy converting and wave eliminating performance than the other existing systems. Its maximum conversion efficiency ξ is up to 70%, while its transmission coefficient Ct is less than 0.45. Compared to M1 and M2, M3 can improve the efficiency ξ of the system (up to approximately 35%) and reduce the maximum force exerted on the system (up to 10%). When studying the forces on the combined system, the horizontal force (in the direction of wave propagation) can be primarily calculated by considering the force exerted on the chamber. However, when investigating the vertical forces (opposite to gravity) acting on the system, both the OWC chamber and the wave-eliminator need to be considered. The research results can provide valuable references for further design optimization of OWC devices.

Trap-assisted tunneling in type II Ag2O/β-Ga2O3 self-powered solar blind photodetector

This study investigates a high-performance Ag2O/β-Ga2O3 self-powered photodiode through experimental and modeling approaches. Initially, a p-type Ag2O film, with a bandgap close to 4 eV and a hole density of approximately 6.35×1018 cm−3, was fabricated using faced two-target sputtering. The conduction and valence band offsets between Ag2O and β-Ga2O3 were determined via X-ray photoelectron spectroscopy, confirming a type II heterojunction. The device had a low on-voltage of 1.50 V and a low on-resistance of 5.40 mΩ.cm² in the dark. Subsequent illumination at 254 nm resulted in a notably high photocurrent, responsivity, and detectivity. To confirm the role of trap-assisted tunneling in the type II Ag2O/β-Ga2O3 heterojunction, Silvaco simulations were employed to model both the dark current and photocurrent. These simulations confirmed the prevalence of the trap-assisted tunneling mechanism, particularly through energy levels situated above the equilibrium Fermi level at the Ag2O/β-Ga2O3 interface, as described by the Danielsson model. Understanding the transport mechanism is paramount for the development of high-performance photodetectors. By comprehending how charge carriers navigate through the device and the influence of traps on their behavior, researchers can optimize device design and fabrication processes to enhance performance.

Influence of the Grid Inclination Angle on the Efficiency of Preliminary Separation of the Combed Heap

Introduction. A highly topical scientific and practical task is to justify the choice of the most advanced variant of the device for pre-separating the combed heap and to optimize its design. Aim of the Study. The study is aimed at testing the effect of the grid inclination angle on the effectiveness of pre-separating the combed heap. Materials and Methods. It is possible to minimize the separated grain flow into the threshing machine either by means of a feeder house grid bottom or by means of a horizontal separating device, located directly in front of the threshing drum. To select the optimal design of the separation device, there was planned and conducted a laboratory experiment. The study was carried out in two stages. In the first series of experiments, the elevator body was installed horizontally, and in the second series – at an angle of 45° to the horizon. The width of the grid bottom openings had four variations (6, 8, 10 and 12 mm), and the speed of the scraper conveyor was 3 m/s. A combed heap of the winter wheat variety Moskovskaya 56 was selected as the study object. The grain moisture was 12%. Results.According to the results of laboratory studies, it was found that with an increase in the width of the grid surface openings, the intensity of separating grain increases. The maximum device throughput (90%) corresponds to the horizontal position of the grid bottom of the experimental installation and the width of the openings b = 12 mm. Extrapolation of the separated grain decreasing graph indicates that to ensure complete separation of grain, the length of the separating grid should be at least L = 0.9 m. Discussion and Conclusion. The use of a horizontal separating grid allows increasing the device separation efficiency and reducing its metal content by 14–16%.

A Predictive Toxicokinetic Model for Nickel Leaching from Vascular Stents.

In vitro testing methods offer valuable insights into the corrosion vulnerability of metal implants and enable prompt comparison between devices. However, they fall short in predicting the extent of leaching and the biodistribution of implant byproducts under in vivo conditions. Physiologically based toxicokinetic (PBTK) models are capable of quantitatively establishing such correlations and therefore provide a powerful tool in advancing nonclinical methods to test medical implants and assess patient exposure to implant debris. In this study, we present a multicompartment PBTK model and a simulation engine for toxicological risk assessment of vascular stents. The mathematical model consists of a detailed set of constitutive equations that describe the transfer of nickel ions from the device to peri-implant tissue and circulation and the nickel mass exchange between blood and the various tissues/organs and excreta. Model parameterization was performed using (1) in-house-produced data from immersion testing to compute the device-specific diffusion parameters and (2) full-scale animal in situ implantation studies to extract the mammalian-specific biokinetic functions that characterize the time-dependent biodistribution of the released ions. The PBTK model was put to the test using a simulation engine to estimate the concentration-time profiles, along with confidence intervals through probabilistic Monte Carlo, of nickel ions leaching from the implanted devices and determine if permissible exposure limits are exceeded. The model-derived output demonstrated prognostic conformity with reported experimental data, indicating that it may provide the basis for the broader use of modeling and simulation tools to guide the optimal design of implantable devices in compliance with exposure limits and other regulatory requirements.

Design of Electrical Sheet Resistance of Thin Film Measurement System Based on GM Cryocooler in Cryogenic Temperature

The determination of the dependencies of the electrical resistivity of the thin film to temperature is of great importance both for understanding the conduction mechanism and for numerous technical applications of these films. In this work, to characterize, the electrical properties of thin films, a GM cryocooler-based automatic board temperature range electrical properties measurement system has been constructed. The system can measure multiple samples simultaneously. The cooling process was simulated using the time-discrete differencing to validate the optimized device design parameters and minimize heat losses. Furthermore, the temperature-dependent sheet resistance results were compared with the results from the physical property measurement system.

Electroosmotic microchannel flow of blood conveying copper and cupric nanoparticles: Ciliary motion experiencing entropy generation using backpropagated networks

AbstractA novel mathematical model for a hybrid (Cu–CuO/blood) Jeffrey nanofluid passing a vertical symmetric microchannel along with an electroosmosis pump is presented. The focuses on the advancement of mathematical modeling techniques, its comprehensive analysis of microfluidic system dynamics, and its potential to inform the optimal design of devices using nanofluids with broad applications in various fields. Arrhenius's law is used to analyze endothermic–exothermic reactions and activation energy. The governing partial differential equations of the fixed frame are transformed into ordinary differential equations of the wave frame using self‐similarity transformations. Low Reynolds number and long‐wavelength approximations helped to find solutions of the equations by applying a suitable BVP solver in MATLAB. The fluid's velocity, temperature, concentration, and electroosmosis properties are studied graphically. Two‐dimensional contour plots of fluid velocity and three‐dimensional surface plots of fluid properties are discussed. Physically significant quantities of mass transfer rate, skin friction coefficient, entropy generation, and heat transfer rate are studied using contour plots. Artificial neural network simulation using Bayesian regularization backpropagation algorithms is analyzed for training state, error histogram, fit, performance, and regression plots. Conclusively, the comprehensive analysis of the fluid dynamics, entropy generation, mass and heat transfer, and in the microchannel, coupled with the successful implementation of artificial neural network simulation, contributes to an improved understanding of the system's behavior. Entropy generation was raised for enhanced Brownian motion number and reduced values of thermophoresis, activation energy, and endothermic–exothermic reaction parameters. This study's results can be used to improve the efficiency and effectiveness of microfluidic devices used in fields as diverse as electrical cooling and medicine delivery.

A Bioinspired and Cost-Effective Device for Minimally Invasive Blood Sampling.

Conventional venipuncture is invasive and challenging in low and middle-income countries. Conversely, point-of-care devices paired with fingersticks, although less invasive, suffer from high variability and low blood volume collection. Recently approved microsampling devices address some of these issues but remain cost-prohibitive for resource-limited settings. In this work, a cost-effective microsampling device is described for the collection of liquid blood with minimal invasiveness and sufficient volume retrieval for laboratory analyses or immediate point-of-care testing. Inspired by the anatomy of sanguivorous leeches, the single-use device features a storage compartment for blood collection and a microneedle patch hidden within a suction cup. Finite Element Method simulations, corroborated by mechanical analyses, guide the material selection for device fabrication and design optimization. In piglets, the device successfully collects ≈195µL of blood with minimal invasiveness. Additionally, a tailor-made lid and adapter enable safe fluid transportation and integration with commercially available point-of-care systems for on-site analyses, respectively. Taken together, the proposed platform holds significant promise for enhancing healthcare in the pediatric population by improving patient compliance and reducing the risk of needlestick injuries through concealed microneedles. Most importantly, given its cost-effective fabrication, the open-source microsampling device may have a meaningful impact in resource-limited healthcare settings.

Thermal Impact Investigation of Shaded Opaque Walls in Bangkok

The effectiveness of overhang shading devices in mitigating solar radiation on opaque walls is crucial for passive building design in tropical regions. However, opaque walls commonly are side-lined in shading design due to a preoccupation with insulation properties, appearance, and aesthetics. Unprotected walls exposed to solar radiation maximize heat gain, while shaded walls decrease external heat gain and indoor temperature, resulting in improved indoor thermal comfort and well-being. This research employs EnergyPlus and SPSS to investigate the optimal design of overhang shading devices for opaque walls in Bangkok, Thailand. The investigation is divided into two phases: (1) determination of the optimal depth-to-space ratio of overhang devices based on a parametric study of 12 overhang configurations; (2) an evaluation of the ratio in reducing solar radiation. The findings demonstrate a relationship between the device depths and the wall solar radiation and indoor temperature. The optimum depth-to-space ratio is suggested as 1:1.15, which can lower the annual average indoor temperature by 3.53% (1.18°C) compared to the benchmark design. Deeper shading device depths than the optimum depth-to-space ratio can be applied, with a slight improvement in protective performance (and commensurately increased costs). This research underscores the importance of overhang shading devices as a performance-improving strategy for buildings in the tropical climate of Thailand.

Design and Parameter Optimization of Conveying and Baling Devices for Ramie Cutting and Baling Machine

Conveying and baling are two important links in the mechanized harvesting of ramie, in the face of ramie cutting and baling harvesting technology research gaps, low stalk conveying rate, high breaking rate and other problems. In this paper, according to the technical requirements of ramie harvesting, we designed a conveying and baling device, the hand-held ramie cutter. First, the key mechanism of the conveying and baling device of the equipment was designed. Then, we analyzed the location of stem clogging and the reasons for the breaking problem during the conveying and baling process. The field harvesting experiments were carried out according to the principles of Box–Behnken experimental design. Taking the machine travelling speed, conveying speed and ramie raking frequency as the test factors and using the Design-Expert V8.0.6.1 to process the data, we established a regression model for each experimental factor on the conveying rate and breaking rate. The order of influence of several factors on the breaking rate is: X2 > X1 > X3; and the effects of the three factors on the conveying rate were X3 > X2 > X1. Through response surface analysis (RSA), the effects of the factors on the indicators were explained, as was the impact of the factors on the indicators. Finally, the parameter optimization was carried out with the delivery rate as the core index. The best combination of motion parameters was obtained as follows: the travelling speed was 0.37 m/s, the chain conveying speed was 1.1 m/s, and the raking frequency was 144 times/min. With the combination of parameters under the field test verification, the results show that compared with the original work quality, the stalk delivery rate increased from 85.2% to 93% (an increase of 7.8%), the stalk breaking rate fell from 31.1% to 20.4% (a decrease of 10.7%). The performance of ramie harvesting and baling was greatly improved, and we achieved relatively satisfactory results.

Machine Learning-based Inverse Design Model of a Transistor

Objectives: To develop an inverse design model for transistors, utilizing machine learning algorithms to predict key design parameters specifically, the length and width based on specified gain and bandwidth requirements. And to conduct a comprehensive comparative analysis with existing literature, evaluating the efficacy and novelty of the proposed model in the context of semiconductor engineering challenges and methodologies. Methods: The comprehensive dataset, comprising 30,000 values generated through LTspice simulations, forms the basis for training the machine learning model. Utilizing a Random Forest regressor as the base model and a multi-output regressor as the main model, the project involves extensive data analysis, model development, and iterative fine-tuning. Findings: The outcomes demonstrate the efficacy of the developed model in accurately predicting transistor dimensions. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, highlight the precision of the model in fulfilling the specified objectives. Novelty: This study introduces a novel approach to semiconductor device design optimization, showcasing the potential of machine learning to streamline the inverse design process. The use of a multi-output regressor, feature engineering, and fine-tuning through log transformation contribute to the innovative nature of the developed model. Keywords: Machine Learning (ML) model, Random Forest regressor, multi­output regressor, Feature engineering, Fine­tuning

Optimal design of bone tumor ablation device based on radio frequency heating using Taguchi method.

This study investigated the optimal design of a radio-frequency (RF) bone tumor ablation device to achieve uniform heating. In a previous study, we confirmed the feasibility of this device, which could heat all regions of the bone to 70°C or higher and maintain this temperature for more than 30min. However, the temperature in each part was non-uniform. To address this issue, the shape of the electrode must be modified to create a uniform electric field. The design of the electrode was optimized to reduce temperature deviations. It is difficult to analytically model the relationship between the shape of the electrode and the electric field. The electrode's design factors were fine-tuned using the Taguchi method, a robust design of experiment approach. The primary objective in this optimization was to maximize the signal-to-noise ratio for temperature in each component, aiming for higher values. After four trials, the signal-to-noise ratio increased in comparison with the initial modified shape from 68.3 to 98.6. The experiment was conducted using an experimental device fabricated using the optimal design factors. In comparison to the previous experiment, the temperature standard deviation per part over time decreased from 10.56°C 4.28°C. The experimental results demonstrated the validity of the proposed optimal design approach. In the future, the proposed method can be used to optimize the design factors when a product is advanced to develop a device that can be applied to the human body.

Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network

AbstractHydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short‐term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems.

Reliability Instability Assessment with Interfacial Trapping Analysis for the Optimization of Al Composition in AlxGa1−xN/GaN High Electron Mobility Transistors

This study investigates the trapping characteristics present at the interface of AlxGa1−xN/GaN high electron mobility transistors (HEMTs) and explores the influence of the Al composition within the AlxGa1−xN barrier on the device's performance. Using a single pulse ID–VD characterization technique, the reliability instability is evaluated in two different AlxGa1−xN/GaN HEMTs: one with an Al composition of 25% (Al0.25Ga0.75N/GaN) and the other with an Al composition of 45% (Al0.45Ga0.55N/GaN). The results unveil a notable higher drain current degradation (ΔID) in Al0.45Ga0.55N/GaN devices, attributed to fast transient charge trapping. Conversely, the Al0.25Ga0.75N/GaN device exhibits a substantial 35% reduction in interface trap density (Dit) and an impressive 73% decrease in border trap density (Nbt), solidifying its reduced trapping behavior compared to Al0.45Ga0.55N/GaN due to the rougher barrier/channel interface of the latter device. Furthermore, load‐pull measurements unveiled a noteworthy 60% power‐added efficiency for the Al0.25Ga0.75N/GaN device, presenting a 10% performance improvement over the Al0.45Ga0.55N/GaN device. These findings provide valuable insights into the trapping phenomena within AlGaN/GaN HEMTs, paving the way for enhanced device design and performance optimization.

Research on optimal design of needle-shaped tower grounding device using thin-shell theory

Research on optimal design of needle-shaped tower grounding device using thin-shell theory

Molecular dynamics study of boiling heat transfer in nanochannels under capillary flow

The increasing heat flux of electronic devices poses significant challenges in thermal management. Passive heat dissipation technology based on capillary drive has been widely concerned because it does not require external driving force and stability. However, due to the small size of the internal capillary structure, typically in the micro and nano scales, and the significant scale effect, further research is needed to understand the mechanism of internal boiling heat transfer. This study systematically investigates the boiling process of liquid in nanochannels under capillary flow with varying wettability using molecular dynamics simulations. It is found that increasing the energy coefficient (c) causes a greater accumulation of atoms near the wall, thereby enhancing the efficiency of solid–liquid heat transfer. The bubble has a larger stable volume as c increases and will burst in the case of high c. As c increases, the region of high potential and kinetic energy expands more rapidly, reducing the time it takes for bubble nucleation and rupture. The behavior of bubbles is significantly influenced by capillary flow, which is also influenced by wettability. When c = 0.9, the flow velocity reaches its maximum before bubble nucleation occurs. It leads to a more homogeneous heat transfer, resulting in a higher location of bubble nucleation. Furthermore, the presence of capillary forces increases the average potential energy of surrounding liquid atoms, thereby enhancing the growth rate of the bubble. This study provides a theoretical basis for the optimal design of capillary-driven two-phase heat dissipation devices.

Design automation of microfluidic single and double emulsion droplets with machine learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.

Local fractional Duffing equation: Its periodic property and its application to energy harvesting

A local fractional modification of the Duffing equation is considered, and the homotopy perturbation method is employed to reveal its frequency-amplitude relationship, which is of paramount importance in the optimal design of the energy harvesting devices and chatter detection. Effects of the initial conditions on the periodic property is also discussed.

Finite Element Beam Propagation Method Based on Coordinate Transformation for Optimal Design of Optical Waveguide Devices

Finite Element Beam Propagation Method Based on Coordinate Transformation for Optimal Design of Optical Waveguide Devices