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Heuristic algorithms to solve the capacity allocation problem in photolithography area (CAPPA)

机译:启发式算法来解决光刻领域(CAPPA)中的容量分配问题

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摘要

Wafer fabrication is one of the most complex and high competence manufacturing. How to fully utilize the machine capacity to meet customer demand is a very important topic. In this paper, we address the capacity allocation problem for photolithography area (CAPPA), which belongs to a capacity requirement planning scheme, with the process window and machine dedication restrictions that arise from an advanced wafer fabrication technology environment. Process window means that a wafer needs to be processed on machines that can satisfy its process capability (process specification). Machine dedication means that once the first critical layer of a wafer lot is processed on a certain machine, the subsequent critical layers of this lot must be processed on the same machine to ensure good quality of final products. We present six modified heuristics and a linear-programming-based heuristic algorithm (LPBHA) to solve the problem efficiently. The performance of the proposed algorithms is tested using real-world CAPPA cases taken from wafer fabrication photolithography area. Computational results show that LPBHA is the most effective one, and with a least average and a least standard deviation of deviation ratio of 0.294 and 0.085% compared to the lower bound of the CAPPA.
机译:晶圆制造是最复杂和最高能力的制造之一。如何充分利用机器容量来满足客户需求是一个非常重要的话题。在本文中,我们解决了光刻面积(CAPPA)的容量分配问题,该问题属于一种容量需求计划方案,其处理窗口和机器专用限制是由先进的晶圆制造技术环境引起的。工艺窗口意味着需要在满足其加工能力(工艺规格)的机器上加工晶圆。机器专用性意味着一旦在特定机器上处理了晶圆批次的第一个关键层,就必须在同一台机器上处理该批次的后续关键层,以确保最终产品的良好质量。我们提出了六种改进的启发式算法和基于线性编程的启发式算法(LPBHA)以有效解决问题。使用从晶圆制造光刻领域获得的实际CAPPA案例对所提出算法的性能进行了测试。计算结果表明,与CAPPA的下限相比,LPBHA是最有效的,偏差率的最小平均值和最小标准偏差为0.294和0.085%。

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