A review of data mining applications in semiconductor manufacturing

Pedro Espadinha-Cruz, Radu Godina, Eduardo M. G. Rodrigues

Research output: Contribution to journalReview articlepeer-review

47 Citations (Scopus)
5 Downloads (Pure)


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.

Original languageEnglish
Article number305
Pages (from-to)1-38
Number of pages38
Issue number2
Publication statusPublished - 6 Feb 2021


  • Data mining
  • Fault detection
  • Process control
  • Quality control
  • Semiconductor manufacturing
  • Yield improvement


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