What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction

Marco Maggipinto, Gian Antonio Susto, Federico Zocco, Sean McLoone

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

Abstract

In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages1796-1801
Number of pages6
ISBN (Electronic)9781728103556
DOIs
Publication statusPublished - 19 Sep 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: 22 Aug 201926 Aug 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period22/08/201926/08/2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Maggipinto, M., Susto, G. A., Zocco, F., & McLoone, S. (2019). What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 1796-1801). [8842942] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8842942