半導體製造產品組合變異與效用評估

Product Mix Variation and Utility Evaluation in Semiconductor Manufacturing Industry

張毓(Yu-Heng Chang)

指導教授 ： 周雍強

國立臺灣大學/工學院/工業工程學研究所/碩士(2004年)

https://doi.org/10.6342/NTU.2004.02004

摘要

對半導體製造產業來說，產能規劃是極重要的決策工作，除了因為機台成本昂貴，一旦規劃失準的結果將嚴重影響廠商的獲利能力外，冗長的前置時間更提高產能擴充決策的風險，加上半導體產品的生命週期短暫，製程技術更迭亦十分迅速，種種因素使得產能規劃工作的困難度更形提高。

在需求變化不確定的環境下，半導體製造廠商所面對的產品變異與日俱增，其中除了一般人所熟知的需求總量變異外，還包括半導體製造業因為產品種類繁多與製造途程複雜所帶來的產品組合變異，尤其對製造廠進行中期產能規劃來說，經常性變動的產品組合所帶來的影響更是顯著，如果不能在需求情境建模的過程中將其考慮進去，那麼據此進行產能規劃的結果可能會在穩定性的表現上大打折扣。
另外為了持續增進供應鏈的競爭力，廠商在選擇合作夥伴時不再能只以品質或成本的角度來衡量，而必須從降低供應鏈風險變異的觀點出發，透過本研究所提出績效評估的程序方法，可供廠商對多個可合作伙伴進行汰弱留強的篩選程序。

關鍵字

產品組合變異 ； 機台需求變異 ； 機台組態規劃 ； 績效評估

並列摘要

Capacity planning is an important tasks in the semiconductor manufacturing industry. Due to the tool purchase cost, firm’s profitability will be deeply eroded once making wrong capacity decision. The risk of capacity expansion is also exacerbated by long lead time of deployment. Together with short life time of product, rapid transition of technology, capacity planning task gets even more complicated.
Under the uncertain environment, demand variation becomes more and more enormous. In addition to the well-known total demand variation, product mix variation, coming from numerous product type and complicated process routing, has far more influence on capacity planning. Without considering product mix variation in scenario representation, the capacity planning can not conduct sound and well.
On the other hand, in order to enhance competitiveness in semiconductor supply chain, firms can’t judge the partners with regard of quality or cost any more. It should be conducted in view of overall risk reduction. This study propose and develop a performance evaluation procedure, firms can use it for screening out less performed partner.

並列關鍵字

Tool portfolio planning ； tool requirement variation ； Product mix variety ； performance evaluation

參考文獻

2. Barahona, Francisco, Stuart Bermon, Oktay Gunluk, and Sarah Hood, “Robust Capacity Planning in Semiconductor Manufacturing,” Research Report RC22196, IBM, 2001.

3. Benavides, Dario L., James R. Duley, and Blake E. Johnson, “As Good as It Gets: Optimal Fab Design and Deployment,” IEEE Transactions on Semiconductor Manufacturing, Vol. 12, No. 3, pp. 281-287, 1999.

4. Cakanyildirim, Metin and Robin O. Roundy, “Evaluation of Capacity Planning Practices for the Semiconductor Industry,” IEEE Transactions on Semiconductor Manufacturing, Vol. 15, No. 3, pp. 331-340, 2002.

5. Chang, P. L., M.G. Huang and Y. C. Chou, “A Customer-driven Capacity Exchange Mechanism for Semiconductor Foundry,” Proc. of 2002 Int. Conference on Modeling and Analysis of Semiconductor Manufacturing, pp.145-147, 2002

6. Chen, Zhi-Long, Shanling Li, and Devanath Tirupati, “A Scenario-based Stochastic Programming Approach for Technology and Capacity Planning,” Computers & Operations Research 29, pp. 781-806, 2002.