Related papers: Characterizing Germanium Junction Transistors
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing in GNNs, their…
With the new generation of the RPC, it is possible to work with induced signals of hundreds $\mu V$, hence the front-end electronics is an important and delicate part of the detector in order to get a detectable signal. The electronic chain…
The Poisson-Nernst-Planck (PNP) equations are fundamental for modeling ion transport in electrochemical systems, capturing the intricate interplay of concentration gradients, electric fields, and ion fluxes essential for applications such…
Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically…
The origin of nonproportionality in scintillator materials has been a long standing problem for more than four decades. In this manuscript, we show that, with the help of first principle modeling, the parameterization of the…
Choosing the number of mixture components remains an elusive challenge. Model selection criteria can be either overly liberal or conservative and return poorly-separated components of limited practical use. We formalize non-local priors…
The performance of strained silicon as the channel material for transistors has plateaued. Motivated by increasing charge-carrier mobility within the device channel to improve transistor performance, germanium (Ge) is considered as an…
Germanium nanowires with p- and n-dopants were synthesized by chemical vapor deposition and used to construct complementary field effect transistors . Electrical transport and x-ray photoelectron spectroscopy data are correlated to glean…
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and…
Deep level transient spectroscopy shows that defects created by alpha irradiation of germanium are annealed by low energy plasma ions up to a depth of several thousand lattice units. The plasma ions have energies of 2-8eV and therefore can…
The time difference between a particle interaction in a Superheated Superconducting Granule (SSG) and the resulting phase transition signal has been explored. Detectors containing Zn and Sn SSG were irradiated with neutrons and protons to…
This study investigates artifact detection in clinical photoplethysmogram signals using Transformer-based models. Recent findings have shown that in detecting artifacts from the Pediatric Critical Care Unit at CHU Sainte-Justine (CHUSJ),…
Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fundamental…
Early-warning indicators (increase of autocorrelation and variance) are commonly applied to time series data to try and detect tipping points of real-world systems. The theory behind these indicators originates from approximating the…
We address the fabrication of nano-architectures by impacting thin layers of amorphous Ge deposited on SiO$_{2}$ with a Ga$^{+}$ ion beam and investigate the structural and optical properties of the resulting patterns. By adjusting beam…
The electrical and photodiode characteristics of ensemble and single p-GaN nanowire and n-Si heterojunction devices were studied. Ideality factor of the single nanowire p-GaN/n-Si device was found to be about three times lower compared to…
Transistors are core component across all domains of electrical and electronic engineering (EEE), such as data centers, electrified transportation, robotics, renewables and grid applications, etc. Transistors' switching behavior governs…
We report on the fabrication and current-voltage (IV) characteristics of very narrow, strip-like arrays of metal nanoparticles. The arrays were formed from gold nanocrystals self-assembled between in-plane electrodes. Local cross-linking of…
Graph neural networks (GNNs) have shown promise in learning the ground-state electronic properties of materials, subverting ab initio density functional theory (DFT) calculations when the underlying lattices can be represented as small…
Predictive Business Process Monitoring (PBPM) aims to forecast future events in ongoing cases based on historical event logs. While Graph Neural Networks (GNNs) are well suited to capture structural dependencies in process data, existing…