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Effect of Fabrication Parameters on the Ferroelectricity of Hafnium Zirconium Oxide Films: A Statistical Study
Get PDFFerroelectricity in hafnium zirconium oxide (Hf1−xZrxO2) and the factors that impact it have been a popular research topic since its discovery in 2011. Although the general trends are known, the interactions between fabrication parameters and their effect on the ferroelectricity of Hf1−xZrxO2 require further investigation. In this paper, we present a statistical study and a model that relates Zr concentration (x), film thickness (tf), and annealing temperature (Ta) with the remanent polarization (Pr) in tungsten (W)-capped Hf1−xZrxO2. This work involved the fabrication and characterization of 36 samples containing multiple sets of metal-ferroelectric-metal capacitors while varying x (0.26, 0.48, and 0.57), tf (10 and 19 nm), and Ta (300, 400, 500, and 600 °C). In addition to the well-understood effects of x and Ta on the ferroelectricity of Hf1−xZrxO2, the statistical analysis showed that thicker Hf1−xZrxO2 films or films with higher x require lower Ta to crystallize and demonstrated that there is no statistical difference between samples annealed to 500 and 600 °C, thus suggesting that most films fully crystallize with Ta ∼ 500 °C for 60 s. Our model explains 95% of the variability in the Pr data for the films fabricated, presents the estimates of the phase composition of the film, and provides a starting point for selecting fabrication parameters when a specific Pr is desired
Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
Get PDFQuantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x–36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error
CuMONITOR: Continuous Monitoring of Microarchitecture for Software Task Identification and Classification
No full textThe interactions between software and hardware are increasingly important to computer system security. This research collected microprocessor control signal sequences to develop machine learning models that identify software tasks. In contrast with prior work that relies on hardware performance counters to collect data for task identification, this research is based on creating additional digital logic to record sequences of control signals inside a processor’s microarchitecture. The proposed approach considers software task identification in hardware as a general problem, with attacks treated as a subset of software tasks. Three lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during actual (i.e., non-simulated) system operation. Second, experimental design selects hardware and software configurations to train and evaluate machine learning models. The machine learning models significantly outperform a naïve classifier based on Euclidean distances from class means. Various experiment configurations produced a range of balanced accuracy scores. Third, task classification is addressed using decision boundaries defined with thresholds chosen by an optimization strategy to develop non-neural network classifiers. When implemented in hardware, the non-neural network classifiers could require less digital logic to implement compared to neural network models
Unlocking Heat Transfer Potential in Multi-sided Porous Geometries: A Study on Magnetothermal Effects and Entropy Generation
No full textThis study investigates heat transfer efficiency and irreversibility generation in multi-sided porous thermal systems filled with a Cu-Al2O3-water hybrid nanofluid. The research examines convective heat transfer across various polygonal geometries, ranging from four-sided to infinitely-sided (circular) structures, aiming to maximize thermal system performance intended for scientific and industrial applications. For a standardized comparison, the fluid volume and active heating and cooling lengths are kept constant for all the geometries considered. The analysis includes varying flow-controlling variables like the modified Rayleigh number (Ram), Hartmann number (Ha), and Darcy number (Da) to evaluate how they affect heat transfer efficiency and entropy generation, including total, magnetic, and viscous entropy. The numerical simulations, conducted using the finite element approach, explore Ram values ranging from 10 to 104, Ha from 0 to 70, and Da from 10-4 to 10-2. Streamline and isothermal contours analyze flow behavior, while heatline tools reveal thermal energy transport dynamics. Results show up to a 20 % improvement in heat transfer efficiency in optimized configurations. Key findings indicate that hexagonal structures can be viable alternatives to circular tubes in space-constrained applications, offering comparable thermal performance. Interestingly, pentagonal cavities exhibit the highest irreversibility due to symmetry loss. The study offers insightful information for enhancing thermal system design in various industrial settings, particularly for applications requiring efficient heat transfer in limited spaces. © 2024 The Author(s
Effects of Fluid Slip on Heat Transfer in the Thin Film Region in a Microchannel
Get PDFA theoretical study was undertaken to investigate the influence of interfacial slip on evaporation of a thin liquid film in a microfluidic channel. The disjoining pressure and the capillary force which drive the liquid flow at the liquid-vapor interface in thin film region are adopted. The evaporating thin film region is an extended meniscus beyond the apparent contact line at a liquid/solid interface. Thin film evaporation plays a key role in a highly efficient heat pipe. Slip length was found to affect the heat transfer in the microchannel by altering the thin film geometry
Building credibility for human systems integration in model‐based systems engineering
No full textExcerpt: Robust and trusted digital human representations are necessary to successfully account for human considerations in model-based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility
Simulating autonomous drone behaviors in an anti-access area denial (A2AD) environment
No full textArmy senior military leaders are invested in acquiring modernized aerial platforms and equipment to augment the US Army’s ability to overcome Anti-Access Area Denial (A2AD) threats imposed by modern Integrated Air Defense Systems (IADS). A prominent element of this modernization effort is the employment of autonomous drones to defeat IADS threats while minimizing risk to Army Soldiers. This research utilizes a framework for classifying the levels of autonomous capability along three dimensions: the ability to act alone, the ability to cooperate, and the ability to adapt. A virtual combat model, created using the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), simulates the engagement between an enemy IADS and a friendly formation comprised of autonomous drones, attack helicopters, and a Long Range Precision Fires (LRPF) capability. A designed experiment evaluates drone performance with varying levels of autonomy. The experimental results reveal that low levels of autonomy yield a 20.74% increase in survivability and a 5.52% increase in lethality
Convective Asymmetries in Thermal Blooming Experiments
No full textSimulation and experiment of steady-state thermal blooming of a laser within a confined propagation chamber are compared. The global fluid response to asymmetric laser heating induces local asymmetries in the beam irradiance profile after propagation
Laser Based Three-Dimensional Imaging of a Hypersonic Sphere’s Chemically Reacting Wake
No full textChemically reacting hypersonic wakes are not well understood. 200 kHz Planar Laser Induced Fluorescence was utilized to investigate NO concentrations behind Mach 10 spheres. Observations were taken up to 60 base diameters into the wake
