Prediction of integral type failures in semiconductor manufacturing through classification methods

Gian Antonio Susto; Sean McLoone; Daniele Pagano; Andrea Schirru; Simone Pampuri; Alessandro Beghi

doi:10.1109/ETFA.2013.6648127

Prediction of integral type failures in semiconductor manufacturing through classification methods

Gian Antonio Susto, Sean McLoone, Daniele Pagano, Andrea Schirru, Simone Pampuri, Alessandro Beghi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Citations (Scopus)

Abstract

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

Original language	English
Title of host publication	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
DOIs	https://doi.org/10.1109/ETFA.2013.6648127
Publication status	Published - 24 Dec 2013
Event	2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 - Cagliari, Italy Duration: 10 Sept 2013 → 13 Sept 2013

Conference

Conference	2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
Country/Territory	Italy
City	Cagliari
Period	10/09/2013 → 13/09/2013

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Industrial and Manufacturing Engineering
Computer Science Applications

Access to Document

10.1109/ETFA.2013.6648127

Cite this

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title = "Prediction of integral type failures in semiconductor manufacturing through classification methods",

abstract = "Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.",

author = "Susto, {Gian Antonio} and Sean McLoone and Daniele Pagano and Andrea Schirru and Simone Pampuri and Alessandro Beghi",

year = "2013",

month = dec,

day = "24",

doi = "10.1109/ETFA.2013.6648127",

language = "English",

isbn = "9781479908622",

booktitle = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",

note = "2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 ; Conference date: 10-09-2013 Through 13-09-2013",

}

Susto, GA, McLoone, S, Pagano, D, Schirru, A, Pampuri, S & Beghi, A 2013, Prediction of integral type failures in semiconductor manufacturing through classification methods. in IEEE International Conference on Emerging Technologies and Factory Automation, ETFA., 6648127, 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013, Cagliari, Italy, 10/09/2013. https://doi.org/10.1109/ETFA.2013.6648127

TY - GEN

T1 - Prediction of integral type failures in semiconductor manufacturing through classification methods

AU - Susto, Gian Antonio

AU - McLoone, Sean

AU - Pagano, Daniele

AU - Schirru, Andrea

AU - Pampuri, Simone

AU - Beghi, Alessandro

PY - 2013/12/24

Y1 - 2013/12/24

N2 - Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

AB - Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

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U2 - 10.1109/ETFA.2013.6648127

DO - 10.1109/ETFA.2013.6648127

M3 - Conference contribution

AN - SCOPUS:84890749806

SN - 9781479908622

BT - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

T2 - 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013

Y2 - 10 September 2013 through 13 September 2013

ER -

Prediction of integral type failures in semiconductor manufacturing through classification methods

Abstract

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