Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)

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Comparison of Classification Machine Learning Models for Production Flow Analysis in a Semiconductor Fab

Authors
Ivan Kristianto Singgih1, 2, 3, *, Stefanus Soegiharto1, Arida Ferti Syafiandini4, 5
1Department of Industrial Engineering, University of Surabaya, Surabaya, Indonesia
2The Indonesian Researcher Association in South Korea (APIK), Seoul, 07342, South Korea
3Kolaborasi Riset dan Inovasi Industri Kecerdasan Artifisial (KORIKA), Jakarta, Indonesia
4Department of Library and Information Science, Yonsei University, Seoul, South Korea
5Research Center for Computing, National Research and Innovation Agency, Indonesia (BRIN), Cibinong, Indonesia
*Corresponding author. Email: ivanksinggih@staff.ubaya.ac.id
Corresponding Author
Ivan Kristianto Singgih
Available Online 19 November 2023.
DOI
10.2991/978-94-6463-288-0_24How to use a DOI?
Keywords
Semiconductor Fab; Classification; Prediction; Machine Learning; Model Evaluation
Abstract

A semiconductor fab has complex wafer lot movements between machines and workstations. To ensure a smooth flow of the wafer lots, the system must be observed appropriately. Observation of such a complicated system is possible using machine learning. In this study, various machine learning techniques are applied to predict the semiconductor fab’s throughput when considering wafer lot processing and queuing status at the machines and the machine utilization. The accuracies of the models are compared. It is shown that the random forest model obtained the best accuracy of more than 97%. Compared with the previous study, this study considers more models to allow a more comprehensive evaluation. The findings are important for providing suggestions on machine learning model selection for predicting the output of a semiconductor fab.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
Series
Atlantis Highlights in Engineering
Publication Date
19 November 2023
ISBN
978-94-6463-288-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-288-0_24How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ivan Kristianto Singgih
AU  - Stefanus Soegiharto
AU  - Arida Ferti Syafiandini
PY  - 2023
DA  - 2023/11/19
TI  - Comparison of Classification Machine Learning Models for Production Flow Analysis in a Semiconductor Fab
BT  - Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
PB  - Atlantis Press
SP  - 268
EP  - 276
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-288-0_24
DO  - 10.2991/978-94-6463-288-0_24
ID  - Singgih2023
ER  -