Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Ou, Feiyang; Wang, Henrik; Zhang, Chao; Tom, Matthew; Bom, Sthitie; Davis, James F.; Christofides, Panagiotis D.

doi:10.1016/j.dche.2024.100195

Title: Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Journal Article · 30 November 2024 · Digital Chemical Engineering

DOI: https://doi.org/10.1016/j.dche.2024.100195 · OSTI ID:2473338

Ou, Feiyang;

; Zhang, Chao;

; Bom, Sthitie; Davis, James F.; Christofides, Panagiotis D.

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Sponsoring Organization:: USDOE

OSTI ID:: 2473338

Journal Information:: Digital Chemical Engineering, Journal Name: Digital Chemical Engineering Journal Issue: C Vol. 13; ISSN 2772-5081

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Artificial neural networks for water quality soft-sensing in wastewater treatment: a review Wang, Gongming; Jia, Qing-Shan; Zhou, MengChu Artificial Intelligence Review, Vol. 55, Issue 1 https://doi.org/10.1007/s10462-021-10038-8	journal	June 2021
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Uncertainty-aware soft sensor using Bayesian recurrent neural networks Lee, Minjung; Bae, Jinsoo; Kim, Seoung Bum Advanced Engineering Informatics, Vol. 50 https://doi.org/10.1016/j.aei.2021.101434	journal	October 2021
The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review Perera, Yasith S.; Ratnaweera, D. A. A. C.; Dasanayaka, Chamila H. Engineering Applications of Artificial Intelligence, Vol. 121 https://doi.org/10.1016/j.engappai.2023.105988	journal	May 2023
On the vanishing and exploding gradient problem in Gated Recurrent Units Rehmer, Alexander; Kroll, Andreas IFAC-PapersOnLine, Vol. 53, Issue 2 https://doi.org/10.1016/j.ifacol.2020.12.1342	journal	January 2020
Cross-validation is safe to use King, Ross D.; Orhobor, Oghenejokpeme I.; Taylor, Charles C. Nature Machine Intelligence, Vol. 3, Issue 4 https://doi.org/10.1038/s42256-021-00332-z	journal	April 2021
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word Zhang, Chao; Yella, Jaswanth; Huang, Yu 2021 IEEE International Conference on Big Data (Big Data) https://doi.org/10.1109/BigData52589.2021.9671925	conference	December 2021
A Survey on Deep Learning for Data-Driven Soft Sensors Sun, Qingqiang; Ge, Zhiqiang IEEE Transactions on Industrial Informatics, Vol. 17, Issue 9 https://doi.org/10.1109/TII.2021.3053128	journal	September 2021
Overview of Reliability Testing Elsayed, Elsayed A. IEEE Transactions on Reliability, Vol. 61, Issue 2 https://doi.org/10.1109/TR.2012.2194190	journal	June 2012
Big data in manufacturing: a systematic mapping study O’Donovan, Peter; Leahy, Kevin; Bruton, Ken Journal of Big Data, Vol. 2, Issue 1 https://doi.org/10.1186/s40537-015-0028-x	journal	September 2015
Moore’s Law revisited through Intel chip density Burg, David; Ausubel, Jesse H. PLOS ONE, Vol. 16, Issue 8 https://doi.org/10.1371/journal.pone.0256245	journal	August 2021
Industry 4.0: The Digital German Ideology Fuchs, Christian tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, Vol. 16, Issue 1 https://doi.org/10.31269/triplec.v16i1.1010	journal	February 2018
Overview of Quartz Crystal Microbalance Behavior Analysis and Measurement Na Songkhla, Sawit; Nakamoto, Takamichi Chemosensors, Vol. 9, Issue 12 https://doi.org/10.3390/chemosensors9120350	journal	December 2021
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A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms G., Ranganathan Journal of Innovative Image Processing, Vol. 3, Issue 1 https://doi.org/10.36548/jiip.2021.1.006	journal	April 2021

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