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김병조

Kim, Byungjo
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dc.citation.number 34 -
dc.citation.startPage 344001 -
dc.citation.title JOURNAL OF PHYSICS D-APPLIED PHYSICS -
dc.citation.volume 56 -
dc.contributor.author Ko, Jungmin -
dc.contributor.author Bae, Jinkyu -
dc.contributor.author Park, Minho -
dc.contributor.author Jo, Younghyun -
dc.contributor.author Lee, Hyunjae -
dc.contributor.author Kim, Kyunghyun -
dc.contributor.author Yoo, Suyoung -
dc.contributor.author Nam, Sang Ki -
dc.contributor.author Sung, Dougyong -
dc.contributor.author Kim, Byungjo -
dc.date.accessioned 2024-02-05T10:35:09Z -
dc.date.available 2024-02-05T10:35:09Z -
dc.date.created 2024-02-05 -
dc.date.issued 2023-08 -
dc.description.abstract As semiconductor device structures become more complex and sophisticated, the formation of finer and deeper patterns is required. To achieve a higher yield for mass production as the number of process steps increases and process variables become more diverse, process optimization requires extensive engineering effort to meet the target process requirements, such as uniformity. In this study, we propose an efficient process design framework that can efficiently search for optimal process conditions by combining deep learning (DL) with plasma simulations. To establish the DL model, a dataset was created using a two-dimensional (2D) hybrid plasma equipment model code for an argon inductively coupled plasma system under a given process window. The DL model was implemented and trained using the dataset to learn the functional relationship between the process conditions and their consequential plasma states, which was characterized by 2D field data. The performance of the DL model was confirmed by comparison of the output with the ground truth, validating its high consistency. Moreover, the DL results provide a reasonable interpretation of the fundamental features of plasmas and show a good correlation with the experimental observations in terms of the measured etch rate characteristics. Using the designed DL, an extensive exploration of process variables was conducted to find the optimal processing condition using the multi-objective particle swarm optimization algorithm for the given objective functions of high etch rate and its uniform distribution. The obtained optimal candidates were evaluated and compared to other process conditions experimentally, demonstrating a fairly enhanced etch rate and uniformity at the same time. The proposed computational framework substantially reduced trial-and-error repetitions in tailoring process conditions from a practical perspective. Moreover, it will serve as an effective tool to narrow the processing window, particularly in the early stages of development for advanced equipment and processes. -
dc.identifier.bibliographicCitation JOURNAL OF PHYSICS D-APPLIED PHYSICS, v.56, no.34, pp.344001 -
dc.identifier.doi 10.1088/1361-6463/acd1fd -
dc.identifier.issn 0022-3727 -
dc.identifier.scopusid 2-s2.0-85159554291 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/81287 -
dc.identifier.wosid 000987839600001 -
dc.language 영어 -
dc.publisher IOP Publishing Ltd -
dc.title Computational approach for plasma process optimization combined with deep learning model -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Physics, Applied -
dc.relation.journalResearchArea Physics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor process design framework -
dc.subject.keywordAuthor deep learning -
dc.subject.keywordAuthor plasma simulations -
dc.subject.keywordAuthor particle swarm optimization -
dc.subject.keywordPlus ION ENERGY-DISTRIBUTIONS -
dc.subject.keywordPlus NEURAL-NETWORKS -
dc.subject.keywordPlus UNIFORMITY -

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