NDLI: Proportional hazard model with ℓ Penalization applied to Predictive Maintenance in semiconductor manufacturing

Content Provider	IEEE Xplore Digital Library
Author	Pampuri, S. Schirru, A. De Luca, C. De Nicolao, G.
Copyright Year	2011
Description	Author affiliation: Infineon Technologies Austria AG, Austria (De Luca, C.) \|\| University of Pavia, Italy (Pampuri, S.; Schirru, A.; De Nicolao, G.)
Abstract	The present paper is motivated by the application of Predictive Maintenance (PM) techniques in the semiconductor manufacturing environment: such techniques are able, using process data, to make reliable predictions of residual equipment lifetime. The employment of PM yields positive fallouts on the productive process in form of unscheduled downtime reduction, increased spare parts availability and improved overall production quality. One of the main challenges in PM modeling regards the data-driven assessment of relevant process variables when insufficient expert knowledge is available. In this paper, survival models theory is employed jointly with ℓ penalization techniques: this allows to obtain sparse models able to select the meaningful process variables and simultaneously predict the remaining lifetime of an equipment. Additionally, frailty modeling techniques are employed to concurrently handle several productive equipments of the same type, exploiting their similarities to increase prediction accuracy. The proposed methodology is validated, illustrating promising results, by means of a semiconductor manufacturing dataset.
Starting Page	250
Ending Page	255
File Size	347891
Page Count	6
File Format	PDF
ISBN	9781457717307
ISSN	21618089
e-ISBN	9781457717321
e-ISBN	9781457717314
DOI	10.1109/CASE.2011.6042436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-24
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hazards Predictive models Semiconductor device modeling Manufacturing Predictive maintenance Training
Content Type	Text
Resource Type	Article

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