Semiconductor yield loss' causes identification: A data mining approach

Barkia, Hasna; Boucher, Xavier; Riche, Rodolphe Le; Beaune, Philippe; Girard, Marie-Agnès; Rozier, David

doi:10.1109/ieem.2013.6962530

Cited by 7 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classification techniques in quality control arise as a way to classify defects [83], failures in bin maps [91], or production lots [131]. The exploration of yield loss causes [84] or failure diagnostics [98] is performed using techniques as rule induction, decision trees, and association rules.…”

Section: Discussionmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

View full text Add to dashboard Cite

show abstract

“…This problem of coupling between industrial and information processes is particularly important when managing the frequent changes affecting manufacturing plants in the microelectronics industry (Petruzzi & Garavelli, 2007). In such industrial environments, ISs highly contribute to the efficiency of industrial production (Barkia, Boucher, Le Riche, Beaune, & Girard, 2013;Cardinali, 1992;Gowan and Mathieu, Gowan & Mathieu, 1996;Izza, 2009;Jardim-Goncalves, Grilo, & Steiger-Garcao, 2006), notably for wafer production (hong, Lim, Ceong, & May, 2012;Izza, Vincent, & Burlat, 2008;Leachman & hodges, 1996). The performance of ISs depends on the good or bad adaptation of both IT architecture and infrastructure (earl, 1989).…”

Section: Introductionmentioning

confidence: 99%

Data-driven decision-making for IT capacity: beyond statistical analyses

Lutz

Boucher

2016

Journal of Decision Systems

Self Cite

View full text Add to dashboard Cite

International audienceThis paper reports a research work piece developed in collaboration with the semiconductor wafer production company: STMicroelectronics. This collaborative research programme aimed at implementing statistical methods, so as to improve business decisions focusing on capacity planning for information technology. The current paper presents the specification, and experimentation of a method dedicated to managing the need to integrate rigorously complex contextualisation factors, when developing statistical-based Decision Support Systems (DSS). The key challenge is to increase the end-user acceptance and success rate for DSS developments. To ensure the successful integration of DSS within the user environment, the paper formalises a so-called ‘Contextualisation process’, integrated within a larger decision-aid development framework. This Contextualisation process is specified with three methodological components, respectively ‘Qualitative Contextualisation’, ‘Statistical Modelling and Formalisation’ and ‘User Integration’. This approach is applied by STMicroelectronics, to a case study focusing on a project of change management for the infrastructure of the Information System. Based on the demonstration of the case study results, the added-value of the Contextualisation process are discussed and further perspectives for applied research in DSS are drawn

show abstract