Extreme learning machines for virtual metrology and etch rate prediction

Luca Puggini, Sean McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.
Original languageEnglish
Title of host publication26th Irish Signals and Systems Conference (ISSC), 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781467369749
DOIs
Publication statusPublished - 25 Jun 2015
Event26th Irish Signals and Systems Conference - Carlow, Ireland
Duration: 24 Jun 201525 Jun 2015

Conference

Conference26th Irish Signals and Systems Conference
CountryIreland
CityCarlow
Period24/06/201525/06/2015

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  • Cite this

    Puggini, L., & McLoone, S. (2015). Extreme learning machines for virtual metrology and etch rate prediction. In 26th Irish Signals and Systems Conference (ISSC), 2015 (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISSC.2015.7163771