A Review of Data Mining Applications in Semiconductor Manufacturing

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 dat...
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Autor*in:	Pedro Espadinha-Cruz [verfasserIn] Radu Godina [verfasserIn] Eduardo M. G. Rodrigues [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	data mining semiconductor manufacturing quality control yield improvement fault detection process control

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 9(2021), 305, p 305
Übergeordnetes Werk:	volume:9 ; year:2021 ; number:305, p 305

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr9020305

Katalog-ID:	DOAJ030314208

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allfieldsSound	10.3390/pr9020305 doi (DE-627)DOAJ030314208 (DE-599)DOAJ424e9e550d5042fb8ab4e9c0a18939f2 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Pedro Espadinha-Cruz verfasserin aut A Review of Data Mining Applications in Semiconductor Manufacturing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. data mining semiconductor manufacturing quality control yield improvement fault detection process control Chemical technology Chemistry Radu Godina verfasserin aut Eduardo M. G. Rodrigues verfasserin aut In Processes MDPI AG, 2013 9(2021), 305, p 305 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:9 year:2021 number:305, p 305 https://doi.org/10.3390/pr9020305 kostenfrei https://doaj.org/article/424e9e550d5042fb8ab4e9c0a18939f2 kostenfrei https://www.mdpi.com/2227-9717/9/2/305 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 305, p 305
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callnumber-first	T - Technology
author	Pedro Espadinha-Cruz
spellingShingle	Pedro Espadinha-Cruz misc TP1-1185 misc QD1-999 misc data mining misc semiconductor manufacturing misc quality control misc yield improvement misc fault detection misc process control misc Chemical technology misc Chemistry A Review of Data Mining Applications in Semiconductor Manufacturing
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topic_title	TP1-1185 QD1-999 A Review of Data Mining Applications in Semiconductor Manufacturing data mining semiconductor manufacturing quality control yield improvement fault detection process control
topic	misc TP1-1185 misc QD1-999 misc data mining misc semiconductor manufacturing misc quality control misc yield improvement misc fault detection misc process control misc Chemical technology misc Chemistry
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title_auth	A Review of Data Mining Applications in Semiconductor Manufacturing
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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