Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding

Geert van Kollenburg; P.H.F.M. (Richard) Verhoeven; Mike J. Holenderski; Nirvana Meratnia; Daniele Pagano

doi:10.1109/WSC60868.2023.10408180

Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding

Geert van Kollenburg, P.H.F.M. (Richard) Verhoeven, Mike J. Holenderski, Nirvana Meratnia, Daniele Pagano

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

The complexity of manufacturing process data has made it more challenging to extract useful insights. Data-analytic solutions have therefore become essential for analyzing and optimizing manufacturing processes. Path modeling, also known as structural equation modeling, is a statistical approach that can provide new insights into complex multivariate relationships between process variables from different stages of the manufacturing process. The incorporation of expert process knowledge and subsequent interpretation of model results can facilitate communication between stakeholders, promoting lean manufacturing and achieving the sustainability goals of Industry 5.0. This paper describes the use of a path modeling algorithm called Process Partial Least Squares (Process PLS) to gain new insights into the relationships between equipment data from several machines within the semiconductor manufacturing process. The methods used in this study can assist manufacturers in understanding the relations between different machines and identify the most influential variables that may be used to develop soft-sensors.

Original language	English
Title of host publication	2023 Winter Simulation Conference, WSC 2023
Editors	C.G. Corlu, S.R. Hunter, H. Lam, B.S. Onggo, J. Shortle, B. Biller
Publisher	Institute of Electrical and Electronics Engineers
Pages	2333-2344
Number of pages	12
ISBN (Electronic)	979-8-3503-6966-3
DOIs	https://doi.org/10.1109/WSC60868.2023.10408180
Publication status	Published - 31 Jan 2024
Event	2023 Winter Simulation Conference - San Antonio, United States Duration: 10 Dec 2023 → 13 Dec 2023 https://meetings.informs.org/wordpress/wsc2023/

Conference

Conference	2023 Winter Simulation Conference
Abbreviated title	WSC 2023
Country/Territory	United States
City	San Antonio
Period	10/12/23 → 13/12/23
Internet address	https://meetings.informs.org/wordpress/wsc2023/

Funding

This work was in part supported by ECSEL Joint Undertaking, under grant agreement No 826589. The authors express their gratitude to Paola Giuffre, Caterina Genua and Daniele Li Rosi for their consultation with respect to the manufacturing process and data presented in this paper. OpenAI’s ChatGPT (GPT-4) was used to proofread the presented text, checking for spelling and grammar. In no way were algorithms used to create original content or to generate ideas.

Funders	Funder number
Electronic Components and Systems for European Leadership	826589

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10.1109/WSC60868.2023.10408180

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van Kollenburg, G., Verhoeven, P. H. F. M., Holenderski, M. J., Meratnia, N., & Pagano, D. (2024). Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding. In C. G. Corlu, S. R. Hunter, H. Lam, B. S. Onggo, J. Shortle, & B. Biller (Eds.), 2023 Winter Simulation Conference, WSC 2023 (pp. 2333-2344). Article 10408180 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WSC60868.2023.10408180

van Kollenburg, Geert ; Verhoeven, P.H.F.M. (Richard) ; Holenderski, Mike J. et al. / Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding. 2023 Winter Simulation Conference, WSC 2023. editor / C.G. Corlu ; S.R. Hunter ; H. Lam ; B.S. Onggo ; J. Shortle ; B. Biller. Institute of Electrical and Electronics Engineers, 2024. pp. 2333-2344

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abstract = "The complexity of manufacturing process data has made it more challenging to extract useful insights. Data-analytic solutions have therefore become essential for analyzing and optimizing manufacturing processes. Path modeling, also known as structural equation modeling, is a statistical approach that can provide new insights into complex multivariate relationships between process variables from different stages of the manufacturing process. The incorporation of expert process knowledge and subsequent interpretation of model results can facilitate communication between stakeholders, promoting lean manufacturing and achieving the sustainability goals of Industry 5.0. This paper describes the use of a path modeling algorithm called Process Partial Least Squares (Process PLS) to gain new insights into the relationships between equipment data from several machines within the semiconductor manufacturing process. The methods used in this study can assist manufacturers in understanding the relations between different machines and identify the most influential variables that may be used to develop soft-sensors.",

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van Kollenburg, G , Verhoeven, PHFM , Holenderski, MJ , Meratnia, N & Pagano, D 2024, Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding. in CG Corlu, SR Hunter, H Lam, BS Onggo, J Shortle & B Biller (eds), 2023 Winter Simulation Conference, WSC 2023., 10408180, Institute of Electrical and Electronics Engineers, pp. 2333-2344, 2023 Winter Simulation Conference, San Antonio, Texas, United States, 10/12/23. https://doi.org/10.1109/WSC60868.2023.10408180

Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding. / van Kollenburg, Geert ; Verhoeven, P.H.F.M. (Richard); Holenderski, Mike J. et al.
2023 Winter Simulation Conference, WSC 2023. ed. / C.G. Corlu; S.R. Hunter; H. Lam; B.S. Onggo; J. Shortle; B. Biller. Institute of Electrical and Electronics Engineers, 2024. p. 2333-2344 10408180.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - The complexity of manufacturing process data has made it more challenging to extract useful insights. Data-analytic solutions have therefore become essential for analyzing and optimizing manufacturing processes. Path modeling, also known as structural equation modeling, is a statistical approach that can provide new insights into complex multivariate relationships between process variables from different stages of the manufacturing process. The incorporation of expert process knowledge and subsequent interpretation of model results can facilitate communication between stakeholders, promoting lean manufacturing and achieving the sustainability goals of Industry 5.0. This paper describes the use of a path modeling algorithm called Process Partial Least Squares (Process PLS) to gain new insights into the relationships between equipment data from several machines within the semiconductor manufacturing process. The methods used in this study can assist manufacturers in understanding the relations between different machines and identify the most influential variables that may be used to develop soft-sensors.

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van Kollenburg G , Verhoeven PHFM , Holenderski MJ , Meratnia N, Pagano D. Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding. In Corlu CG, Hunter SR, Lam H, Onggo BS, Shortle J, Biller B, editors, 2023 Winter Simulation Conference, WSC 2023. Institute of Electrical and Electronics Engineers. 2024. p. 2333-2344. 10408180 doi: 10.1109/WSC60868.2023.10408180

Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS to Enhance Process Understanding

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