使用基因演算法求解晶圓重測門檻值之利潤最大化問題

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：49

、訪客IP：3.138.69.101

姓名

翁佩如(Pei-ru Weng) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

使用基因演算法求解晶圓重測門檻值之利潤最大化問題
(Using Genetic Algorithm to Establish Thresholds of Wafer Retesting When Maximizing Profit)

相關論文

★ 以類神經網路探討晶圓測試良率預測與重測指標值之建立	★ 六標準突破性策略—企業管理議題
★ 限制驅導式在製罐產業生產管理之應用研究	★ 應用倒傳遞類神經網路於TFT-LCD G4.5代Cell廠不良問題與解決方法之研究
★ 限制驅導式生產排程在PCBA製程的運用	★ 平衡計分卡規劃與設計之研究-以海軍後勤支援指揮部修護工廠為例
★ 木製框式車身銷售數量之組合預測研究	★ 導入符合綠色產品RoHS之供應商管理-以光通訊產業L公司為例
★ 不同產品及供應商屬性對採購要求之相關性探討－以平面式觸控面板產業為例	★ 中長期產銷規劃之個案探討 -以抽絲產業為例
★ 消耗性部品存貨管理改善研究-以某邏輯測試公司之Socket Pin為例	★ 封裝廠之機台當機修復順序即時判別機制探討
★ 客戶危害限用物質規範研究-以TFT-LCD產業個案公司為例	★ PCB壓合代工業導入ISO/TS16949品質管理系統之研究-以K公司為例
★ 報價流程與價格議價之研究–以機殼產業為例	★ 產品量產前工程變更的分類機制與其可控制性探討-以某一手機產品家族為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

晶圓製造完成後，會針對每片晶圓做完整的晶圓良率及不良狀況測試，確定是否可達成客戶或是公司的期望規格，並且可以提供評估晶圓品質好壞的重要指標，以了解晶圓廠製程品質的穩定性，同時為了提昇測試良率，往往會重測特定不良問題之晶圓。一般來說，不良晶圓的分類方法，是透過 Bin 數字的分類來檢測晶圓上各種不同不良的指標，在分析的過程中，首先測試工程師必須先分析良率是否符合目標值，如果晶圓良率低於目標值，可考慮針對特定不良晶圓執行重測動作。由於不良的原因可能來自於晶圓廠製程異常、測試設備不穩定，或是因測試作業不當而造成；然而大部分工程師所關心的，除了必須分析造成良率異常的原因外，更想了解在重測後晶圓良率是否可提高，期望在重測後的良率比未重測前能明顯提高，因此為了提升晶圓良率，往往只注意良率的變化，而未考量實際所帶來的重測效益。
本研究收集案例公司於 95 年某產品的晶圓測試資料、測試成本與產品利潤之相關資料以供研究分析，論文首先探討案例中影響不良的原因為何，並透過基因演算法尋找最適之重測門檻值，符合最佳的測試流程，也希望為案例公司帶來最佳效益，為此論文之主要貢獻。
實驗的分析顯示，所提出的基因演算法方法得到的重測門檻值最後得到的利潤為13182474，比實際個案公司的重測門檻得到的利潤13144288還多出38186的改進，若是只看需要重測的晶圓，則利潤為2711771.2，比原個案公司的2365971.8改進了114.62%，因此可以說由此研究提出的基因演算法求解晶圓重測門檻值有不錯的結果。

摘要(英)

In order to verify whether wafer can be able to achieve expected specification, generally every die will be probed completely after being fabricated in wafer fab. By such operation, we can use the essential index to improve wafer foundry’s quality and also the yield rate of wafers. In the mean time, IC design house hopes to get better yield rate during wafer probing process, normally they will try to retest low-yield-wafer, no matter retest entire gross dies or particular defective dies.
In general, defective dies are classified by using different bin numbers; the bin numbers represent particular testing result or its performance. During failure analysis after finishing wafer testing, testing engineer can decide to re-test abnormal wafer directly if it’s out of yield limit set previously. As normally engineers just ‘hope’ to get higher yield recovered from second testing, they seldom know how to predict yield variation and regard the related profit before making decision of re-testing
Hence, this thesis attempts to propose a workable solution for wafer testing process by using Genetic Algorithm to establish thresholds of wafer retesting. Through a real example from CMOS Image Sensor probing process, it was presented to demonstrate the methodology.
The result of experiment show that the profit of this study is 13182474, and it is greater than the profit of case company, increases 38186. If we only discuss the retesting wafer, the profit is 2711771.2, compares with case company, and its improvement is 114.62%. So that could say Genetic Algorithm has a not bad solution with threshold of wafer retesting in this thesis.

關鍵字(中)

★ 實數編碼
★ 基因演算法
★ 重測門檻值
★ 晶圓測試

關鍵字(英)

★ real-valued.
★ Wafer testing
★ Thresholds for retesting
★ Genetic Algorithm

論文目次

摘要 i
Abstract ii
致謝 iii
Table of Content iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
1.1 Research motivation and background 1
1.2 Problem description 6
1.3 Research objectives 8
1.4 Research methodology and framework 8
1.4.1 Research methodology 8
1.4.2 Research framework 9
Chapter 2 Literature Review 10
2.1 Wafer Retest 10
2.2 Threshold Value 12
2.3 Genetic Algorithm 14
2.3.1 Encoding 15
2.3.2 Initial Population 17
2.3.4 Selection 18
2.3.5 Crossover 18
2.3.6 Mutation 19
2.3.7 Stopping Condition 19
Chapter 3 Genetic Algorithm 20
3.1 Wafer testing process 20
3.2 Notations list 26
3.3 Encode 28
3.4 Initial Population 29
3.5 Fitness 30
3.6 Selection 30
3.7 Crossover 37
3.8 Mutation 43
3.9 Stopping Condition 45
3.10 Algorithm 46
Chapter 4 Numerical Analysis 53
4.1 Analyzing the raw data 53
4.2 Parameters 54
4.3 Performance of Genetic Algorithm and others 58
Chapter 5 Conclusion 61
5.1 Research Contribution 61
5.2 Research Limitation 61
5.3 Further Research 62
Reference 63
Appendix1. Partial of raw data 66

參考文獻

[1]C. Alonso, C. Andrade, M. Castellote and P. Castro, “Chloride Threshold Values to Depassivate Reinforcing Bars Embedded in a Standardized OPC Mortar,” Elsevier Science, 30, 1047-1055, 2000.
[2]J. E. Baker, “Reducing Bias and Ineffiency in the Selection Algorithm,” Grefensteette, 14-21, 1987.
[3]L. B. Booker, Intelligent Behavior as an Adaptation to the Task Environment, the University of Michigan, 1982.
[4]T. Blickle, and L. Thiele, “A Comparison of Selection Schemes Used in Genetic Algorithms,” TIK-Report, 11(2), Dec., 1995.
[5]C.H. Chen, Using Artificial Neural Network to Predict Wafer Yield and Establish Thresholds of Wafer Retesting, Executive Master of Industrial Management, National Central University, 2006.
[6]H. D. Cheng, J. R. Chen and J.Li, “Threshold Selection based on Fuzzy c-Partition Entropy Approach,” Pattern Recognition, 31(7), 857-870, 1998.
[7]M. Chakraborty and U. K. Chakraborty, “An Analysis of Linear Ranking and Binary Tournament Selection in Genetic Algorithms,” IEEE, 407-411, Sep., 1997.
[8]S. P. Cunninhham, S. MacKinnom, “Statistical Methods for Visual Defect Metrology,” IEEE, 11(1), 48-53, Feb., 1998.
[9]J. Devillers, Genetic algorithms in molecular modeling, ACADEMIC PRESS, 1996.
[10]K. Deb, A. Kumar, “Real-coded genetic algorithms with simulated binary crossover: Studies on multimodal and multi-objective problems,” Complex Systems, vol. 9, no. 6, pp.431-454, 1995.
[11]C. P. Erick, “Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms,” Journal of Heuristics, 7, 311-334, Jul, 2001.
[12]D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, 1993&1989.
[13]B. Hari., et al., “Genetic Algorithm Based Scheduling of Parallel Batch Machines with Incompatible Job Families to Minimize Total Weighted Tardiness,” International Journal of Production Research, 42(8), 1621-1638, April 2004.
[14]H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michihgan Press, 1975.
[15]S.C. Hong, S. Y. Lin and M. H. Cheng, “Reducing the Overkills and Retests in Wafer Testing Process,” IEEE/SEMI Advanced Manufacturing Conference, 286-291, 2003.
[16]S. S. Han., and G.. S. May., “Optimization of Neural Network Structure and Learning Parameters Using Genetic Algorithm,”IEEE, 200-206, 1996.
[17]A. Jubai., B. Jing. and J. Yang., “Combining Fuzzy Theory and Genetic Algorithm for Satellite Image Edge Detection,” International Journal of Remote Sensing, 27(14), 3013-3024, July, 2006.
[18]I.C. Ku., Statistical Approaches to Yield Forecast and Yield Mining of IC Manufacturing, Master of Statistics, NTCU, 2002.
[19]J. S. Lin., Using Artificial Neural Network for Wafer Test Yield Prediction, Executive Master of Industrial Management, National Cheng Kung University, 2004.
[20]S. Y. Lin and S. C. Hong, “Application of an Ordinal Optimization Algorithm to the Wafer Testing Process,” IEEE, 36(6), Nov., 2006.
[21]H. Muhlenbein, “The Equation for Response to Selection and Its Use for Prediction,” Evolutionary Computation, 5(3), 303-346, 1998.
[22]T. Murata, H. Ishibuchi and H. Tanaka, “Genetic Algorithms for Flowshop Scheduling Problems,” Computers ind. Engng, 30(4), 1061-1071, 1996.
[23]M. Srinivas, “Genetic Algorithms: A Survey,” IEEE Computer, 17-26, Jul., 1994.
[24]M. Srinivas, and L. M. Patnaik, “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms,” IEEE, 24(4), 656-667, April, 1994.
[25]K. S. Tang, et al., “Genetic Algorithms and their Application,” IEEE, 22-37, Nov., 1996.
[26]B. Wu and C. L. Chung, “Using Genetic Algorithm to Parameters (d, r) Estimation for Threshold Autoregressive Models,” Elsevier Science Computational Statistics & Data Analysis, 38, 315-330, 2002.

指導教授

沈國基(Gwo-gi Sheen)

審核日期

2008-7-15

推文