By Lars Mönch, John W. Fowler, Scott J. Mason
Over the final fifty-plus years, the elevated complexity and pace of built-in circuits have significantly replaced our international. this day, semiconductor production is likely to be crucial phase of the worldwide production area. because the semiconductor has develop into extra aggressive, enhancing making plans and regulate has develop into a key issue for enterprise good fortune. This booklet is dedicated to creation making plans and keep an eye on difficulties in semiconductor wafer fabrication amenities. it's the first publication that takes a entire examine the position of modeling, research, and comparable info structures for such production structures. The booklet presents an operations learn- and desktop science-based creation into this crucial box of semiconductor manufacturing-related research.
Read Online or Download Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems PDF
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Additional resources for Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems
Sample text
The processing time of all jobs within a family is the same. The combination of decreased line widths and more area per wafer in 300mm wafer fabs results in fewer wafers being needed to fill an IC order of a customer. Each wafer fab will have only a limited number of FOUPs as they are expensive. A large number of FOUPs have the potential to cause the AMHS to become overloaded. In addition, some machines have the same processing times regardless of the number of wafers in the batch. 2 Front-End and Back-End Operations 23 is not reasonable to assign an individual FOUP to each order.
N. 7). The function Q(x) is the expected second-stage value function and will be discussed later in more detail. • b ∈ IRm is the column vector of the right-hand side of the m constraints. • A is an IRm×n matrix. 9) where the second-stage objective function, also called the recourse function, is defined as follows: ˜ ξ (ω )) := min {q(ω )y|y ∈ Y } . 10) ˜ ξ (ω )) is the objective function value of a second linear program with the Q(x, set of feasible solutions p Y := y ∈ IR+ |Wy = h(ω ) − T (ω )x .
These known optimum values can be used to assess the performance of heuristics. When the branching tree is truncated, we obtain beam search heuristics (cf. Pinedo [240]). The truncation of the branching tree helps to reduce the computational effort. 3 Mixed Integer Programming We introduce MIP formulations as a generalization of linear programming approaches [25, 214, 243]. A linear MIP model is an optimization model with a linear objective function that contains real- and integer-valued decision variables and linear constraints.