Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks

Jian Wan, Seán McLoone

Research output: Chapter in Book/Published conference outputChapter


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.
Original languageEnglish
Title of host publicationElectronics, Communications and Networks
Subtitle of host publicationProceedings of the 13th International Conference (CECNet 2023), Macao, China, 17–20 November 2023
EditorsAntonio J. Tallón-Ballesteros, Estefanía Cortés-Ancos, Diego A. López-García
Pages68 - 74
ISBN (Electronic)9781643684819
ISBN (Print)9781643684802
Publication statusPublished - 20 Nov 2023
Event13th International Conference (CECNet 2023) - Macao, China
Duration: 17 Nov 202320 Nov 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications


Conference13th International Conference (CECNet 2023)

Bibliographical note

© 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).


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