透過您的圖書館登入
IP:3.12.162.65
  • 學位論文

半導體廠黃光區派工紫式決策架構

UNISON Framework for Dispatching Problem of Photolithography Area in Semiconductor Manufacturing

指導教授 : 簡禎富
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


黃光區域機台的微影製程常是系統瓶頸所在,其機台的派工對於協調全廠其它區域與平衡工作負荷,有著極重大的影響。半導體產業的資本額主要投資集中於機台設備,產能運用成為維持競爭力最主要的關鍵因素。為了有效地運用產能,制定合適的生產規劃與排程派工在半導體產業中是顯著的影響因素之一。除了成本的壓力外,由於競爭日益激烈,半導體產業將更加重視客戶的滿意度。高達交率成為重要的競爭因素之一,派工與排程策略成為增加競爭優勢的關鍵。然而過去相關研究缺乏依據現場實際狀況、動態決定最適派工的決策機制,且由於半導體產業的派工問題複雜且考量因素眾多,單一法則或單一目標的派工模式已不適用,轉以考量多個目標同時進行規劃已成為當前所需探究的課題。本研究考慮黃光區域機台派工,發展黃光區域機台派工決策模型之決策分析架構,利用系統化的多目標技術規劃、分析並求解半導體產業面臨的黃光區域機台派工問題,以最佳派工策略來達到半導體廠提升產能利用、降低人力成本、滿足客戶需求之目的。並以半導體產業為實證對象,實證結果顯示較案例公司的利用經驗法則派工為佳。本研究結果可以幫助企業擬定最適派工策略來達成實務上所重視之生產目標,提升企業關注之生產指標水準,達到企業理想的目標,提升整體之競爭力。

並列摘要


Photolithography machines are the bottleneck of a wafer fab, its dispatching has a significant effect on loading balance of other area. Effectively and efficiently utilize the bottleneck is important to improve tool productivity and maintain the competitive advantage of operation efficiency, production management in the photolithography has become an important issue of semiconductor manufacturing. In addition, customer satisfaction has become an important issue in the semiconductor industry due to the increasing competition. High hit rate become one of the important competitive factors, scheduling and dispatching strategy are critical for increasing competitive advantage. Thus, this study aims to construct a decision framework for dispatching decision model in photolithography area. The proposed framework based on multi objective genetic algoruthm approach to plan, analysis and slove dispatching problem in photolithography area which derive the best strategy to enhance the capacity utitlize, reduce labor cost and sutusfy the customers need. Empirical study has been done to sort the strategy to fufill the operation target and improve predormance for overall competeiveness. The results have proved the validity of the proposed framework.

參考文獻


Shr, A., Liu, A., and Chen, P. P., (2008), “Load Balancing Among Photolithography Machines in the Semiconductor Manufacturing System,” Journal of Information Science and Engineering, Vol. 24, No, 2, pp. 379-391.
Akcali, E., Nemoto, K., and Uzoy, R., (2001), “Cycle-Time Improvements for Photolithography Process in Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 14, No. 1, pp. 48-56.
Arisha, A. and Young, P., (2004), “Intelligent simulation-based lot scheduling of photolithography toolsets in a wafer fabrication facility,” Proceedings of the 2004 Winter Simulation Conference, Washington, DC, December 5-8.
Bhandari, D., Murthy, C. A., and Pal, S. K., (1996), “Genetic Algorithm with elitist model and its convergence,” International Journal of Pattern Recognition and Artificial Intelligence, Vol.10, No. 6, pp. 731-747.
Cakici, E. and Mason, S. J., (2007), “Parallel machine scheduling subject to auxiliary resource constraints,” Production Planning & Control: The Management of Operations, Vol. 18, No. 3, pp. 217-225.

被引用紀錄


Guo, H. Z. (2017). 應用兩階段解碼之遺傳演算法於 TFT-LCD陣列製造之動態排程問題 [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-0401201816025011

延伸閱讀