Improving Cluster Tool Performance by Finding the Optimal Sequence and Cyclic Sequence of Wafer Handler Moves

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2000

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The research aims to develop algorithms that can minimize the total lot processing time (makespan) of cluster tools used for semiconductor manufacturing. Previous research focuses on finding an optimal sequence of wafer handler moves in a cluster tool that has one process chamber in each stage. In practice, if the number of chambers in a stage is more than one, either a pre-specified sequence of moves is given in advance or a dispatching rule is applied. No previous work has addressed the problem of finding an optimal sequence of wafer handler moves to improve performance of cluster tools with more than one chamber in a stage. Cluster tools are highly integrated machines that can perform a sequence of semiconductor manufacturing processes. The performance of cluster tools becomes increasingly important as the semiconductor industry produces larger wafers with smaller device geometry. Some factors that motivate the use of cluster tools, instead of stand-alone tools, include increased yield and throughput, less contamination, and less human intervention. In this research, the cluster tool is modeled as a manufacturing system with a material handling system (wafer handler). The model specifies all constraints that a feasible sequence of wafer handler moves must satisfy. The thesis develops two cluster tool scheduling algorithms. Given the lot size, the wafer handler move time, the in-chamber processing times, and the tool configuration the first algorithm, based on a complete forward branch-and-bound algorithm, searches for an optimal solution from the set of all feasible sequences of wafer handler moves. The second algorithm, a truncated branch-and-bound algorithm, quickly searches for the best solution from the set of feasible cyclic sequences of wafer handler moves. For simple tool configurations, analytical makespan models are also derived. The results show that, in many cases, the search algorithms can significantly reduce the total lot processing time. This reduces tool utilization, reduces manufacturing cycle times, and increases tool capacity.

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