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

doi:10.1109/COASE.2019.8842942

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 proceeding › Conference 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 language	English
Title of host publication	2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
Publisher	IEEE Computer Society
Pages	1796-1801
Number of pages	6
ISBN (Electronic)	9781728103556
DOIs	https://doi.org/10.1109/COASE.2019.8842942
Publication status	Published - 19 Sep 2019
Event	15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada Duration: 22 Aug 2019 → 26 Aug 2019

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2019-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Country/Territory	Canada
City	Vancouver
Period	22/08/2019 → 26/08/2019

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/COASE.2019.8842942Licence: Unspecified

Advances in machine learning for sustainable manufacturing
Author: Zocco, F., Dec 2021
Supervisor: Liu, X. (Supervisor) & McLoone, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy

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

Maggipinto, Marco ; Susto, Gian Antonio ; Zocco, Federico ; McLoone, Sean. / What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction. 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, 2019. pp. 1796-1801 (IEEE International Conference on Automation Science and Engineering).

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

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.",

author = "Marco Maggipinto and Susto, {Gian Antonio} and Federico Zocco and Sean McLoone",

year = "2019",

month = sep,

day = "19",

doi = "10.1109/COASE.2019.8842942",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

pages = "1796--1801",

booktitle = "2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019",

note = "15th IEEE International Conference on Automation Science and Engineering, CASE 2019 ; Conference date: 22-08-2019 Through 26-08-2019",

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Maggipinto, M, Susto, GA, 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., 8842942, IEEE International Conference on Automation Science and Engineering, vol. 2019-August, IEEE Computer Society, pp. 1796-1801, 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, Vancouver, Canada, 22/08/2019. https://doi.org/10.1109/COASE.2019.8842942

What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction. / Maggipinto, Marco; Susto, Gian Antonio; Zocco, Federico; McLoone, Sean.

2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, 2019. p. 1796-1801 8842942 (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August).

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

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T1 - What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction

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Maggipinto M, Susto GA, Zocco F, McLoone S. 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. IEEE Computer Society. 2019. p. 1796-1801. 8842942. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/COASE.2019.8842942

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

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Fingerprint

Student Theses

Advances in machine learning for sustainable manufacturing

Cite this