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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.06.007 Byline: Tao Yuan (a), Way Kuo (b) Keywords: Quality control; Stochastic processes; Pattern recognition; Semiconductor manufacturing 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. Author Affiliation: (a) Department of Industrial and Information Engineering, The University of Tennessee, Knoxville, TN 37996, United States (b) College of Engineering, The University of Tennessee, 124 Perkins Hall, Knoxville, TN 37996, United States Article History: Received 24 August 2006; Accepted 3 June 2007

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.11.002 Byline: J. Ashayeri (a), W. Selen (b) Keywords: Production; Scheduling; Sequencing; Printed circuit boards Abstract: A planning and scheduling model is developed for the onsertion-phase in printed circuit board (PCB) assembly. PCBs are manufactured by machines that mount components, such as chips, on a PCB. On an onsertor machine, a certain number of component types can be stored. These components are subsequently used for the production of a certain PCB batch type. This research develops a production planning and scheduling formulation to determine the component-machine allocations, as well as a PCB sequence. Two strategies are proposed; one focusing on minimal number of changeovers (C-strategy), and the other on minimal process time (P-strategy). Both strategies were validated in a pilot study with real company data, with encouraging results for potential implementation. Author Affiliation: (a) Department of Econometrics and Operations Research, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands (b) Institute for Logistics and Supply Chain Management, VictorEia University, Melbourne VIC 8001, Australia Article History: Received 17 January 2005; Accepted 1 November 2006