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/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publication Electronics, Communications and Networks. Proceedings of the 13th International Conference (CECNet 2023)
EditorsAntonio J. Tallón-Ballesteros, Estefanía Cortés-Ancos, Diego A. López-García
PublisherIOS Press
Pages68 - 74
Volume381
ISBN (Electronic)9781643684819
ISBN (Print)9781643684802
DOIs
Publication statusPublished - 12 Jan 2024
Event13th International Conference (CECNet 2023) - Macao, China
Duration: 17 Nov 202320 Nov 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
ISSN (Print)0922-6389

Conference

Conference13th International Conference (CECNet 2023)
Country/TerritoryChina
CityMacao
Period17/11/202320/11/2023

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