6 citations · 6 across the 5 of their papers we have counts for
5 papers
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
Manasa Kaniselvan, Mauro Dossena, Denghui Lu +4
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport…
Optimizing Semiconductor Device Simulations through Low-Precision Arithmetic
Alexander Maeder, Denghui Lu, Nicolas Vetsch +6
Architectural changes in GPUs, especially the promotion of low-precision computational units, pose significant challenges to traditional, FP64-based high-performance computing (HPC…
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
Mathieu Luisier, Nicolas Vetsch, Alexander Maeder +8
The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo…
Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance
Nicolas Vetsch, Alexander Maeder, Vincent Maillou +7
Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quant…
Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation
Leonard Deuschle, Jiang Cao, Alexandros Nikolaos Ziogas +4
The continuous scaling of metal-oxide-semiconductor field-effect transistors (MOSFETs) has led to device geometries where charged carriers are increasingly confined to ever smaller…