6 citations · 10 across the 11 of their papers we have counts for
11 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…
Simulation of exchange coupling effects in double quantum dot FinFET-like structures
Ilan Bouquet, Alexander Maeder, Mathieu Luisier
By leveraging a GPU-accelerated Schrödinger-Poisson (SP) solver, we characterize exchange coupling in a hole spin double-qubit device involving a double quantum dot (DQD) system fo…
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2
Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extend…
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…
EmuGEMM: Fused Tensor Core Kernels for Precision Emulation in Matrix Multiplication
Denghui Lu, Alexander Maeder, Mathieu Luisier +1
Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Scheme…
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…
Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction
Manasa Kaniselvan, Alexander Maeder, Chen Hao Xia +2
Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales, enabling…
Learning the Electronic Hamiltonian of Large Atomic Structures
Chen Hao Xia, Manasa Kaniselvan, Alexandros Nikolaos Ziogas +4
Graph neural networks (GNNs) have shown promise in learning the ground-state electronic properties of materials, subverting ab initio density functional theory (DFT) calculations w…
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…
Influence of Carrier-Carrier Interactions on the Sub-threshold Swing of Band-to-Band Tunnelling Transistors
Chen Hao Xia, Leonard Deuschle, Jiang Cao +2
Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely…