There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 5371 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 5364 | - |
dc.citation.title | IEEE TRANSACTIONS ON ELECTRON DEVICES | - |
dc.citation.volume | 68 | - |
dc.contributor.author | Jeong, Changwook | - |
dc.contributor.author | Myung, Sanghoon | - |
dc.contributor.author | Huh, In | - |
dc.contributor.author | Choi, Byungseon | - |
dc.contributor.author | Kim, Jinwoo | - |
dc.contributor.author | Jang, Hyunjae | - |
dc.contributor.author | Lee, Hojoon | - |
dc.contributor.author | Park, Daeyoung | - |
dc.contributor.author | Lee, Kyuhun | - |
dc.contributor.author | Jang, Wonik | - |
dc.contributor.author | Ryu, Jisu | - |
dc.contributor.author | Cha, Moon-Hyun | - |
dc.contributor.author | Choe, Jae Myung | - |
dc.contributor.author | Shim, Munbo | - |
dc.contributor.author | Kim, Dae Sin | - |
dc.date.accessioned | 2023-12-21T15:06:58Z | - |
dc.date.available | 2023-12-21T15:06:58Z | - |
dc.date.created | 2022-04-01 | - |
dc.date.issued | 2021-11 | - |
dc.description.abstract | There is a growing consensus that the physics-based model needs to be coupled with machine learning (ML) model relying on data or vice versa in order to fully exploit their combined strengths to address scientific or engineering problems that cannot be solved separately. We propose several methodologies of bridging technology computer-aided design (TCAD) simulation and artificial intelligence (AI) with its application to the tasks for which traditional TCAD faces challenges in terms of simulation runtime, coverage, and so on. AI-emulator that learns fine-grained information from rigorous TCAD enables simulation of process technologies and device in real-time as well as large-scale simulation such as full-pattern analysis of stress without high demand on computational resource. To accelerate atomistic molecular dynamics (MD) simulation, we have done a comparison study of descriptor-based and graph-based neural net potential, and also show their capability with large-scale and long-time simulation of silicon oxidation. Finally, we discuss the use of hybrid modeling of AI- and physics-based model for the case where physical equations are either fully or partially unknown. | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON ELECTRON DEVICES, v.68, no.11, pp.5364 - 5371 | - |
dc.identifier.doi | 10.1109/TED.2021.3093844 | - |
dc.identifier.issn | 0018-9383 | - |
dc.identifier.scopusid | 2-s2.0-85110884252 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/58461 | - |
dc.identifier.wosid | 000711645500009 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Bridging TCAD and AI: Its Application to Semiconductor Design | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic; Physics, Applied | - |
dc.relation.journalResearchArea | Engineering; Physics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Semiconductor process modeling | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Mathematical model | - |
dc.subject.keywordAuthor | Semiconductor device modeling | - |
dc.subject.keywordAuthor | device simulation | - |
dc.subject.keywordAuthor | full-chip level modeling | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | process simulation | - |
dc.subject.keywordAuthor | semiconductor | - |
dc.subject.keywordAuthor | technology computer-aided design (TCAD) | - |
dc.subject.keywordAuthor | Numerical models | - |
dc.subject.keywordAuthor | Analytical models | - |
dc.subject.keywordAuthor | Artificial intelligence (AI) | - |
dc.subject.keywordAuthor | atomistic simulation | - |
dc.subject.keywordAuthor | design optimization | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.