Gummel-cycle Algebraic Multigrid Preconditioning for Large-scale Device Simulations

Koshimoto, Hiroo; Ishimabushi, Hisashi; Yoo, Jaehyun; Kayama, Yasuyuki; Yamada, S.; Kwon, Uihui; Kim, Dae Sin

doi:10.23919/sispad49475.2020.9241643

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Other1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As semiconductor technologies have become more sophisticated, TCAD simulation gets more time-consuming. To reduce simulation time, there have been attempts to use some technologies [1], [2] based on multicore computing, which turned out to be insufficient. As an alternative way, researchers began introducing deep learning (DL) models.…”

Section: Introductionmentioning

confidence: 99%

Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Myung

Jang

Jin

et al. 2021

2021 IEEE International Electron Devices Meeting (IEDM)

Self Cite

View full text Add to dashboard Cite

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge -a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithmpredicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.

show abstract

Section: Introductionmentioning

confidence: 99%