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

Singgih, Ivan Kristianto and Soegiharto, Stefanus and Syafiandini, Arida Ferti (2023) Comparison of Classification Machine Learning Models for Production Flow Analysis in a Semiconductor Fab. In: Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023), 14-15 September 2023, Yogyakarta.

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Semiconductor Fab, Classification, Prediction, Machine Learning, Model Evaluation.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Depositing User:	IVAN KRISTIANTO SINGGIH
Date Deposited:	23 Nov 2023 04:55
Last Modified:	23 Nov 2023 04:55
URI:	http://repository.ubaya.ac.id/id/eprint/45408

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