Wan, Jian and McLoone, Seán (2023). Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks. IN: Electronics, Communications and Networks. Tallón-Ballesteros, Antonio J.; Cortés-Ancos, Estefanía and López-García, Diego A. (eds) Frontiers in Artificial Intelligence and Applications, 381 . CHN: IOS.
Abstract
Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OES) data from the endpoint detection system. Simulation studies demonstrate the feasibility and the effectiveness of the proposed approach.
Publication DOI: | https://doi.org/10.3233/faia231177 |
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Divisions: | College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing College of Engineering & Physical Sciences Aston University (General) |
Additional Information: | © 2024 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
Event Title: | 13th International Conference (CECNet 2023) |
Event Type: | Other |
Event Dates: | 2023-11-17 - 2023-11-20 |
ISBN: | 9781643684802, 9781643684819 |
Last Modified: | 04 Dec 2024 08:25 |
Date Deposited: | 17 Jan 2024 11:20 |
Full Text Link: | |
Related URLs: |
https://ebooks. ... 3233/FAIA231177
(Publisher URL) |
PURE Output Type: | Chapter |
Published Date: | 2023-11-20 |
Authors: |
Wan, Jian
(
0000-0001-5904-9261)
McLoone, Seán |