Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing-Reference-Cited by-同舟云学术

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Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

Published:2021-02-18 Issue:6 Volume:36 Page:2618-2638
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J Intell Syst

Author:

Rostami Hamideh¹²^ORCID,Blue Jakey³^ORCID,Chen Argon³^ORCID,Yugma Claude¹^ORCID

Affiliation:

1. Department of Manufacturing Sciences and Logistics Mines Saint‐Etienne, CNRS, UMR 6158 LIMOS, CMP, Univ. Clermont Auvergne Gardanne France

2. Data Science and Engineering Group, ASML Veldhoven The Netherlands

3. Institute of Industrial Engineering, National Taiwan University Taipei Taiwan (Republic of China)

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22395

Reference31 articles.

1. Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration

2. An overview of time-based and condition-based maintenance in industrial application

3. MoyneJ SchulzeB IskandarJ ArmacostM Next generation advanced process control: leveraging big data and prediction. In:27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) 2016:191‐196.

4. Machine Fault Diagnosis and Prognosis: The State of The Art

5. Intelligent Fault Diagnosis and Prognosis for Engineering Systems

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1. Modeling and solving framework for tactical maintenance planning problems with health index considerations;Computers & Operations Research;2024-10

2. A Data and Knowledge Fusion‐Driven Early Fault Warning Method for Traction Control Systems;International Journal of Intelligent Systems;2024-01

3. Incremental Learning and Conditional Drift Adaptation for Nonstationary Industrial Process Fault Diagnosis;IEEE Transactions on Industrial Informatics;2023-04

4. Minimizing the sum of completion times on a single machine with health index and flexible maintenance operations;Computers & Operations Research;2023-03

5. Data-driven Prognostic Approaches for Semiconductor Manufacturing Process: A Review of Recent Works and Future Perspectives;IFAC-PapersOnLine;2023

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