DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Jaewoo | ko |
dc.contributor.author | Kim, Taeyeong | ko |
dc.contributor.author | Lee, Jungchul | ko |
dc.date.accessioned | 2022-12-21T09:00:43Z | - |
dc.date.available | 2022-12-21T09:00:43Z | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.citation | MICRO AND NANO SYSTEMS LETTERS, v.10, no.1 | - |
dc.identifier.issn | 2213-9621 | - |
dc.identifier.uri | http://hdl.handle.net/10203/303447 | - |
dc.description.abstract | Unique self-assembled germanium structures known as Germanium-on-Nothing (GON), which are fabricated via annealing, have buried multiscale cavities with different morphologies. Due to their unique sub-surface morphologies, GON structures are utilized in various applications including optoelectronics, micro-/nanoelectronics, and precision sensors. Each application requires different cavity shapes, and a simulation tool is able to determine the required annealing duration for a given shape. However, a theoretical simulation inevitably requires simplifications which limit its accuracy. Herein, to resolve such dependence on simplification, we introduce a deep learning-based method for simulating the transformation of sub-surface morhpology of GON over annealing. Namely, a deep learning model is trained to predict GON’s morphological transformation from 4 cross-sectional images acquired at different annealing times. Compared to conventional simulation schemes, our proposed deep learning-based simulation method is not only computationally efficient (∼ 10 min) but also physically accurate with its use of empirical data. © 2022, The Author(s). | - |
dc.language | English | - |
dc.publisher | SPRINGERNATURE | - |
dc.title | Simulation of Germanium-on-Nothing cavity’s morphological transformation using deep learning | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85143650940 | - |
dc.type.rims | ART | - |
dc.citation.volume | 10 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | MICRO AND NANO SYSTEMS LETTERS | - |
dc.identifier.doi | 10.1186/s40486-022-00164-5 | - |
dc.contributor.localauthor | Lee, Jungchul | - |
dc.contributor.nonIdAuthor | Jeong, Jaewoo | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Germanium-on-nothing | - |
dc.subject.keywordAuthor | Simulation | - |
dc.subject.keywordPlus | SILICON | - |
dc.subject.keywordPlus | SPACE | - |
dc.subject.keywordPlus | CHANNELS | - |
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