Pseudopotential-based electron quantum transport: Theoretical formulation and application to nanometer-scale silicon nanowire transistors

2016 ◽  
Vol 119 (3) ◽  
pp. 035701 ◽  
Author(s):  
Jingtian Fang ◽  
William G. Vandenberghe ◽  
Bo Fu ◽  
Massimo V. Fischetti
Keyword(s):  
Quantum Transport ◽  
Silicon Nanowire ◽  
Nanometer Scale ◽  
Theoretical Formulation ◽  
Nanowire Transistors ◽  
Electron Quantum

Influence of discrete dopant on quantum transport in silicon nanowire transistors

2012 ◽  
Vol 70 ◽  
pp. 92-100 ◽  
Author(s):  
Nima Dehdashti Akhavan ◽  
Isabelle Ferain ◽  
Ran Yu ◽  
Pedram Razavi ◽  
Jean-Pierre Colinge
Keyword(s):  
Quantum Transport ◽  
Silicon Nanowire ◽  
Nanowire Transistors

scholarly journals Enabling Large-Scale Simulations of Quantum Transport with Manycore Computing

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 253
Author(s):  
Yosang Jeong ◽  
Hoon Ryu
Keyword(s):  
Quantum Transport ◽  
Large Scale ◽  
Performance Enhancement ◽  
Silicon Nanowire ◽  
Matrix Multiplication ◽  
Tight Binding ◽  
Optimization Techniques ◽  
Wide Energy Range ◽  
Processing Unit ◽  
Binding Model

The non-equilibrium Green’s function (NEGF) is being utilized in the field of nanoscience to predict transport behaviors of electronic devices. This work explores how much performance improvement can be driven for quantum transport simulations with the aid of manycore computing, where the core numerical operation involves a recursive process of matrix multiplication. Major techniques adopted for performance enhancement are data restructuring, matrix tiling, thread scheduling, and offload computing, and we present technical details on how they are applied to optimize the performance of simulations in computing hardware, including Intel Xeon Phi Knights Landing (KNL) systems and NVIDIA general purpose graphic processing unit (GPU) devices. With a target structure of a silicon nanowire that consists of 100,000 atoms and is described with an atomistic tight-binding model, the effects of optimization techniques on the performance of simulations are rigorously tested in a KNL node equipped with two Quadro GV100 GPU devices, and we observe that computation is accelerated by a factor of up to ∼20 against the unoptimized case. The feasibility of handling large-scale workloads in a huge computing environment is also examined with nanowire simulations in a wide energy range, where good scalability is procured up to 2048 KNL nodes.

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