Please use this identifier to cite or link to this item:
Title: Hierarchical aggregation/disaggregation for adaptive abstraction-level conversion in digital twin-based smart semiconductor manufacturing
Authors: Seok, Moon Gi
Cai, Wentong
Park, Daejin
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Seok, 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.
Project: A19C1a0018
Journal: IEEE Access
Abstract: In 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.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3073618
Schools: School of Computer Science and Engineering 
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Citations 50

Updated on Jun 2, 2023

Web of ScienceTM
Citations 50

Updated on Jun 2, 2023

Page view(s)

Updated on Jun 8, 2023

Download(s) 50

Updated on Jun 8, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.