dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
San Francisco |
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dc.citation.endPage |
18.2.4 |
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dc.citation.startPage |
18.2.1 |
- |
dc.citation.title |
2021 IEEE International Electron Devices Meeting |
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dc.contributor.author |
Myung, Sanghoon |
- |
dc.contributor.author |
Jang, Wonik |
- |
dc.contributor.author |
Jin, Seonghoon |
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dc.contributor.author |
Choe, Jae Myung |
- |
dc.contributor.author |
Jeong, Changwook |
- |
dc.contributor.author |
Kim, Dae Sin |
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dc.date.accessioned |
2024-01-31T21:06:11Z |
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dc.date.available |
2024-01-31T21:06:11Z |
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dc.date.created |
2022-04-11 |
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dc.date.issued |
2021-12-11 |
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dc.description.abstract |
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 algorithm predicts 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. |
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dc.identifier.bibliographicCitation |
2021 IEEE International Electron Devices Meeting, pp.18.2.1 - 18.2.4 |
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dc.identifier.doi |
10.1109/IEDM19574.2021.9720616 |
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dc.identifier.scopusid |
2-s2.0-85126985843 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/76436 |
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dc.language |
영어 |
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dc.publisher |
Institute of Electrical and Electronics Engineers Inc. |
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dc.title |
Restructuring TCAD System: Teaching Traditional TCAD New Tricks |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2021-12-11 |
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