Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference

Tao Yuan and Way Kuo

European Journal of Operational Research, 2008, vol. 190, issue 1, 228-240

Abstract: Defects on semiconductor wafers tend to cluster and the spatial defect patterns contain useful information about potential problems in the manufacturing process. This study proposes to use model-based clustering algorithms via Bayesian inferences for spatial defect pattern recognition on semiconductor wafers. These new algorithms can find the number of defect clusters as well as identify the pattern of each cluster automatically. They are capable of detecting curvilinear patterns, ellipsoidal patterns and nonuniform global defect patterns. Promising results have been obtained from simulation studies.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377-2217(07)00556-5
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:190:y:2008:i:1:p:228-240

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().