LAPSE:2022.0159
Published Article
LAPSE:2022.0159
A Review of Data Mining Applications in Semiconductor Manufacturing
December 6, 2022
For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.
Keywords
data mining, Fault Detection, process control, quality control, semiconductor manufacturing, yield improvement
Suggested Citation
Espadinha-Cruz P, Godina R, Rodrigues EMG. A Review of Data Mining Applications in Semiconductor Manufacturing. (2022). LAPSE:2022.0159
Author Affiliations
Espadinha-Cruz P: UNIDEMI-Research and Development Unit in Mechanical and Industrial Engineering, Faculty of Science and Technology (FCT), Universidade NOVA de Lisboa, 2829-516 Almada, Portugal [ORCID]
Godina R: UNIDEMI-Research and Development Unit in Mechanical and Industrial Engineering, Faculty of Science and Technology (FCT), Universidade NOVA de Lisboa, 2829-516 Almada, Portugal [ORCID]
Rodrigues EMG: Management and Production Technologies of Northern Aveiro—ESAN, Estrada do Cercal 449, Santiago de Riba-Ul, 3720-509 Oliveira de Azeméis, Portugal [ORCID]
Journal Name
Processes
Volume
9
Issue
2
First Page
305
Year
2021
Publication Date
2021-02-06
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9020305, Publication Type: Review
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LAPSE:2022.0159
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doi:10.3390/pr9020305
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Dec 6, 2022
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CC BY 4.0
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[v1] (Original Submission)
Dec 6, 2022
 
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Dec 6, 2022
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https://psecommunity.org/LAPSE:2022.0159
 
Original Submitter
Mina Naeini
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