TY - JOUR
T1 - Improving convergence and simulation time of quantum hydrodynamic simulation
T2 - Application to extraction of best 10-nm FinFET parameter values
AU - Dai, Xiaoliang
AU - Jha, Niraj K.
N1 - Funding Information:
This work was supported by the National Science Foundation under Grant CCF-1217076.
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2017/1
Y1 - 2017/1
N2 - As electronic devices enter the deep nanometer regime, accurate and efficient device simulations become necessary to account for the emerging quantum effects. The traditional drift-diffusion and hydrodynamic (HD) device simulation models are not accurate in this regime. It is important to use the quantum HD (QHD) simulation model. However, this model suffers from poor convergence and high CPU times. To overcome these obstacles, in this paper, we propose a novel method to replace part of the QHD simulation that exhibits poor convergence behavior and high CPU time with HD simulation. In order to implement this, we capture the device states from the classical HD model and then apply the results as the initial guess to the QHD simulation, which is then solved by the Newton-Raphson method. This leads to significant improvements. The nonconvergence rate and the simulation time are reduced by 86.0% and 30.2%, respectively. As an application of the proposed methodology, we extract the best parameter values of both bulk and silicon-on-insulator FinFETs at the 10-nm technology node from their vast device design space.
AB - As electronic devices enter the deep nanometer regime, accurate and efficient device simulations become necessary to account for the emerging quantum effects. The traditional drift-diffusion and hydrodynamic (HD) device simulation models are not accurate in this regime. It is important to use the quantum HD (QHD) simulation model. However, this model suffers from poor convergence and high CPU times. To overcome these obstacles, in this paper, we propose a novel method to replace part of the QHD simulation that exhibits poor convergence behavior and high CPU time with HD simulation. In order to implement this, we capture the device states from the classical HD model and then apply the results as the initial guess to the QHD simulation, which is then solved by the Newton-Raphson method. This leads to significant improvements. The nonconvergence rate and the simulation time are reduced by 86.0% and 30.2%, respectively. As an application of the proposed methodology, we extract the best parameter values of both bulk and silicon-on-insulator FinFETs at the 10-nm technology node from their vast device design space.
KW - Device simulation
KW - FinFET
KW - hydrodynamic (HD) model
KW - quantum HD (QHD) model
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U2 - 10.1109/TVLSI.2016.2564300
DO - 10.1109/TVLSI.2016.2564300
M3 - Article
AN - SCOPUS:84971434649
SN - 1063-8210
VL - 25
SP - 319
EP - 329
JO - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
JF - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IS - 1
M1 - 7479485
ER -