Tool capacity planning in semiconductor manufacturing

Abstract

The demand for distinct wafer types in semiconductor manufacturing is an explicit function of the electronic components in which these wafers are used. Given that the component demands vary not only by the product type but also over time, it is obvious that wafer demands are also lumpy and time varying. In this paper, we discuss strategic level investment decisions on procuring new equipment and aggregate level capacity planning. In this context, we examine the problem of planning wafer production over multiple time periods within a single facility assuming that a demand forecast for each wafer type for each period is known. To address this problem, we develop a multi-period mixed-integer programming model to minimize the machine tool operating costs, new tool acquizition costs, and inventory holding costs. Given that production of wafers requires a large number of operations with multiple tools capable of performing each operation, tool operating costs are explicitly minimized by integrating the assignment of specific operations to tools in our model. Since our model is computationally intractable, we propose a Lagrangean-based relaxation heuristic to find efficient tool procurement plans. Scope and purpose Semiconductor manufacturing companies are faced with important capital investment decisions for the procurement of new types of machine tools for their facilities. This paper is motivated by the machine tool planning issues faced by such a facility in the US that spends a few million dollars every quarter on procurement of new machine tools. Additional requirements for new tools arise primarily from the replacement of obsolete equipment and the growth in demand for the existing products as well as the introduction of new semiconductor products that require newer technologies. Since most of these tools are very expensive special purpose equipment even a slight enhancement in the management's decision-making process might lead to significant financial improvement in the manufacturer's performance. In this paper, we model a multi-period tool capacity planning problem for given demand forecasts and we present a Lagrangean-based heuristic solution approach to obtain efficient procurement strategies. We also provide computational experiments to test the quality of our results.