Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154073
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dc.contributor.authorSeok, Moon Gien_US
dc.contributor.authorCai, Wentongen_US
dc.contributor.authorPark, Daejinen_US
dc.date.accessioned2022-02-15T02:49:12Z-
dc.date.available2022-02-15T02:49:12Z-
dc.date.issued2021-
dc.identifier.citationSeok, M. G., Cai, W. & Park, D. (2021). Hierarchical aggregation/disaggregation for adaptive abstraction-level conversion in digital twin-based smart semiconductor manufacturing. IEEE Access, 9, 71145-71158. https://dx.doi.org/10.1109/ACCESS.2021.3073618en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://hdl.handle.net/10356/154073-
dc.description.abstractIn smart manufacturing, engineers typically analyze unexpected real-time problems using digitally cloned discrete-event (DE) models for wafer fabrication. To achieve a faster response to problems, it is essential to increase the speed of DE simulations because making optimal decisions for addressing the issues requires repeated simulations. This paper presents a hierarchical aggregation/disaggregation (A/D) method that substitutes complex event-driven operations with two-layered abstracted models-single-group mean-delay models (SMDMs) and multi-group MDMs (MMDMs)-to gain simulation speedup. The SMDM dynamically abstracts a DE machine group's behaviors into observed mean-delay constants when the group converges into a steady state. The MMDM fast-forwards the input lots by bypassing the chained processing steps in multiple steady-state groups until it schedules the lots for delivery to subsequent unsteady groups after corresponding multi-step mean delays. The key component, the abstraction-level converter (ALC), has the roles of MMDM allocation, deallocation, extension, splitting, and controls the flow of each group's input lot by deciding the destination DE model, SMDM, and MMDMs. To maximize the reuse of previously computed multi-step delays for the dynamically changing MMDMs, we propose an efficient method to manage the delays using two-level caches. Each steady-state group's ALC performs statistical testing to detect the lot-arrival change to reactivate the DE model. However, fast-forwarding (FF) results in incorrect test results of the bypassed group's ALCs due to the missed observations of the bypassed lots. Thus, we propose a method for test-sample reinitialization that considers the bypassing. Moreover, since a bypassed group's unexpected divergence can change the multi-step delays of previously scheduled events, a method for examination of FF history is designed to trace the highly influenced events. This proposed method has been applied in various case studies, and it has achieved speedups of up to about 5.9 times, with 2.5 to 8.3% degradation in accuracy.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.language.isoenen_US
dc.relationA19C1a0018en_US
dc.relationRIE2020 IAF-PPen_US
dc.relation.ispartofIEEE Accessen_US
dc.rights© 2021 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleHierarchical aggregation/disaggregation for adaptive abstraction-level conversion in digital twin-based smart semiconductor manufacturingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/ACCESS.2021.3073618-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85104625698-
dc.identifier.volume9en_US
dc.identifier.spage71145en_US
dc.identifier.epage71158en_US
dc.subject.keywordsAbstraction-Level Conversionen_US
dc.subject.keywordsAggregation/Disaggregationen_US
dc.description.acknowledgementThis work was supported in part by the A*STAR Cyber-Physical Production System (CPPS)—Towards Contextual and Intelligent Response Research Program through the RIE2020 IAF-PP under Grant A19C1a0018, in part by the Model Factory@SIMTech, and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Ministry of Science and ICT under Grant NRF2019R1A2C2005099 and Grant NRF2018R1A6A1A03025109.en_US
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