Levels of Capacity and Material Handling System Modeling for Factory Integration Decision Making in Semiconductor Wafer Fabs

• Published in: IEEE transactions on semiconductor manufacturing Vol. 21; no. 4; pp. 600 - 613
• Main Authors: Jimenez, J.A., Mackulak, G.T., Fowler, J.W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Applied sciences; Automated material handling systems (AMHS); Capacity planning; Case studies; Classification; Computational efficiency; Computer simulation; Costs; Decision making; discrete-event simulation; Electronics; Exact sciences and technology; Fabrication; General (including economical and industrial fields); Manufacturing engineering; Materials handling; Microelectronic fabrication (materials and surfaces technology); modeling; Predictive models; Production facilities; Production systems; Semiconductor device modeling; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; Semiconductors; wafer fabrication; Wafers; Performance evaluation; Decision support system; Materials handling; discrete-event simulation; Factory; Modeling; Case study; Production management; Computation time; Microelectronic fabrication; Automated material handling systems (AMHS); capacity planning; Electronics industry; wafer fabrication; Tool management; Wafer; Discrete event system; Execution time
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2008.2005368

As the costs of building a new wafer fab increase, a detailed simulation model representing the production operations, the tools, the automated material handling systems (AMHS), and the tool-AMHS interactions is needed for accurately planning the capacity of these facilities. The problem is that it currently takes too long to build, experiment, and analyze a sufficiently detailed model of a fab. The key for building accurate and computationally efficient fab models is to decide on the right amount of model details, specifically those details representing the equipment capacity and the AMHS. This paper identifies a method for classifying a fab model by the level of capacity detail, the level of AMHS detail, or the level of capacity/AMHS detail. Within the capacity/ AMHS modeling level, our method further differentiates between detailed integrated capacity/AMHS models and abstract coupled capacity/AMHS models. The proposed classification method serves as the basis of a framework that helps users select the system components to be modeled within a desired level of detail. This research also provides a review of past-published literature summarizing the work done at each of the proposed fab modeling levels. A case study comparing the performance between an integrated capacity/AMHS model and a coupled capacity/AMHS model is presented. The study demonstrates that the coupled model generates cycle time estimates that are not statistically different than those generated by the integrated model. This paper also shows that the coupled model can improve CPU time by approximately 98% in relation to the integrated model.