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This paper presents a design and implementation of a high-power Gallium Nitride (GaN)-based multilevel Hbridge inverter to excite wireless charging coils for the wireless power transfer of electric vehicles (EVs). Compared to the…
Ga$_2$O$_3$/SiC heterointegration is attractive for ultra-wide-bandgap power electronics, but interfacial thermal boundary conductance (TBC) remains a major heat-removal bottleneck. Direct experimental access to intrinsic atomistic…
In the whole aircraft structural optimization loop, thermal analysis plays a very important role. But it faces a severe computational burden when directly applying traditional numerical analysis tools, especially when each optimization…
Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component…
The characteristics of a thermal system depend strongly on its response to thermal gradients and the underlying microscopic interactions among constituents. In the present study, we investigate the thermodynamic and transport properties of…
Understanding the atomic-scale mechanisms governing metal-mediated nucleation and growth of gallium nitride (GaN) and related alloys is critical for tailoring their structural and functional properties in advanced electronic,…
We present a new method for calculating the temperature profile in high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting…
Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially…
To achieve high device performance and high reliability for the gallium nitride (GaN)-based high electron mobility transistors (HEMTs), efficient heat dissipation is important but remains challenging. Enormous efforts have been made to…
Graphene has drawn wide attention due to its exceptional thermal conductivity but complete understanding of thermal characteristics of polycrystalline graphene is still elusive to date. For the first time, herein, we have systematically…
The thermal stability and structural evolution of a GaN high-electron-mobility transistor (HEMT) heterostructure grown on a Si (111) substrate were investigated using in situ high-temperature X-ray diffraction (HT-XRD), reciprocal space…
Heterostructures of wurtzite based devices have attracted great research interests since the tremendous success of GaN in light emitting diodes (LED) industry. Among the possible heterostructure material candidates, high quality GaN thin…
Accurate and fast thermophysical models are needed to embed vapor-liquid equilibrium (VLE) calculations in design, optimization, and control loops for cryogenic mixtures. This study asks whether a structure-aware graph neural network (GNN;…
Thermal decomposition of wurtzite (0001)-oriented GaN was analyzed: in vacuum, under active N exposure, and during growth by rf-plasma assisted molecular beam epitaxy. The GaN decomposition rate was determined by measurements of the Ga…
Despite temperature rise being a first-order design constraint, traditional thermal estimation techniques have severe limitations in modeling critical aspects affecting the temperature in modern-day chips. Existing thermal modeling…
The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing…
Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of…
The design of energy-efficient, high-performance, and reliable Convolutional Neural Network (CNN) accelerators involves significant challenges due to complex power and thermal management issues. This paper introduces SAfEPaTh, a novel…
In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has…
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive…