進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-1102201919121700
論文名稱(中文) 以特徵值擷取方式進行管制圖圖形辨識 - 以半導體製程管制之應用為例
論文名稱(英文) Recognition of Control Chart Pattern Using Features Selection - A Case for Process Control in the Semiconductor Industry
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 107
學期 1
出版年 108
研究生(中文) 劉淑民
研究生(英文) Shu-Ming Liu
學號 R37051192
學位類別 碩士
語文別 中文
論文頁數 45頁
口試委員 指導教授-黃宇翔
口試委員-翁慈宗
口試委員-王惠嘉
中文關鍵字 管制圖  特徵值選取  圖形辨識  支援向量機 
英文關鍵字 Control Chart  Features Selection  Pattern Recognition  Support Vector Machine 
學科別分類
中文摘要 傳統 Shewhart管制圖在常態分佈的假設前提下進行管控,而目前半導體產業連續性的自動化生產過程中,其假設並不全盤適用於工廠實際量測的品質特性,某些品質特性呈現特殊趨勢,而管制圖預警模式建立之目的在於提早發現潛在的變異風險。本研究將使用分類準確度高的支援向量機 (Support Vector Machine, SVM) 來建構模型,利用監督式學習的概念對比過去資料或同型態品質特性的資料點,並即時反應變化趨勢,以提醒人員作為異常處理辨識之參考。
本研究採以半導體實際生產數據呈現真實趨勢變化,提出三種於工廠常見異常趨勢圖形做為分類主題,並探究以何種型態特徵值作為變數,例如原始資料、統計特徵值、形狀特徵值或以共變性篩選特徵值,以及需要多少樣本區間或訓練樣本比例更適用於業界生產時異常偵測分析,可協助於變數選擇時決策同時節省成本。同時本研究也揭露了不同的變數組合可增加分類正確性的可能性,不一定需要透過調整分類器參數提高正確性,避免造成過度配適 (Over Fitting) 的問題;且本研究也發現當不同變數組合所提供的分類效果均表現佳且差異不大時,建議在實務應用上亦可選擇較少變數或容易計算之組合,目的為達到高效能低成本的資料處理步驟。
英文摘要 This study uses support vector machine (SVM) to construct the model based on the concept of supervised learning, compares the quality characteristics of the data points in the historical data, and then responds to the alert from the control chart as a reference for identifying abnormal handling in process.

This study uses the real semiconductor manufacturing process data and classifies three common abnormal trend patterns in the factory into cyclic, shift, and trend patterns. We also consider several related variables, such as the raw data, statistical features, shape features, and the selected features with screening eigenvalues to investigate how many sample intervals or training-testing sample ratios are suitable for detecting abnormality during the manufacturing process. This study also reveals the feasibility that different combinations of the related variables can enhance the classification accuracy. It is unnecessary to improve the classification accuracy by adjusting the parameters of the classifier to prevent the occurrence of over fitting problems. We find that when the classification effects from the different combinations of the variables are good or do not vary a lot, using the combination of the less variables for simple calculation is feasible for a practical consideration to achieve high-performance and low-cost data pre-processing steps.
論文目次 目錄
摘要 iii
英文延伸摘要 ix
誌謝 ix
目錄 x
表目錄 xii
圖目錄 xiii
第一章 緒論 1
第一節、研究背景 1
第二節、研究動機 1
第三節、研究目的 2
第四節、研究範圍與重要性 3
第五節、論文架構 3
第二章 文獻回顧 5
第一節、品質管制圖 5
第二節、管制圖圖形辨識 7
第三節、支援向量機分析 10
第四節、特徵值選取 12
第五節、小結 14
第三章 研究方法 15
第一節、 問題描述 15
第二節、研究架構 21
第三節、研究步驟 22
一、特徵值選取 22
二、維度縮減 26
三、分類器選定 27
第四節、預期效益 27
第四章 數值分析與範例 29
第一節、數值範例 29
第二節、研究限制與範圍 30
第三節、研究結果分析 31
第五章 結論與未來方向 41
第一節、研究貢獻 41
第二節、未來方向 41
參考文獻 43

表目錄
表1-1 決策表 2
表4-1 三種圖形樣本敘述統計 29
表4-2 七種特徵值組合分類正確性結果 31
表4-3 以樣本區間分類正確性結果 32
表4-4 以訓練樣本分類正確性結果 33
表4-5 週期圖形以樣本區間分類正確率結果 34
表4-6 偏移圖形以樣本區間分類正確率結果 35
表4-7 趨勢圖形以樣本區間分類正確率結果 36
表4-8 週期圖形以訓練比例分類正確率結果 37
表4-9 偏移圖形以訓練比例分類正確率結果 38
表4-10 趨勢圖形以訓練比例分類正確率結果 39

圖目錄
圖1-1 研究架構圖 4
圖2-1 利用管制圖改善製程 6
圖2-2 區域法則示意圖 6
圖2-3 五種基本管制圖模式 8
圖2-4 六種混和型管制圖模式 9
圖2-5 1991~2010 CCPR文獻比較 12
圖2-6 特徵值輸入比較 13
圖3-1 矽晶圓化學機械拋光機構示意圖 16
圖3-2 週期圖形出現變異示意圖 16
圖3-3 閘級氧化層示意圖 17
圖3-4 閘級氧化層厚度圖形出現偏移示意圖 18
圖3-5 金屬層蝕刻後製程示意圖 19
圖3-6 金屬層蝕刻後線寬圖形出現向上趨勢示意圖 19
圖3-7 分類模型流程圖 21
圖4-1 圖形分類實驗結果 29
圖4-2 異常圖形與特徵值維度比較 31
圖4-3 異常圖形與樣本區間比較 32
圖4-4 異常圖形與訓練比例比較 33
圖4-5 週期圖形與樣本區間比較 34
圖4-6 偏移圖形與樣本區間比較 35
圖4-7 趨勢圖形與樣本區間比較 36
圖4-8 週期圖形與訓練樣本比較 37
圖4-9 偏移圖形與訓練樣本比較 38
圖4-10 趨勢圖形與訓練樣本比較 39

參考文獻 Addeh, J., Ebrahimzadeh, A., Azarbad, M., & Ranaee, V. (2014). Statistical process control using optimized neural networks: A case study. ISA transactions, 53(5), 1489-1499.
Aly, A. A., Saleh, N. A., Mahmoud, M. A., & Woodall, W. H. (2015). A reevaluation of the adaptive exponentially weighted moving average control chart when parameters are estimated. Quality and Reliability Engineering International, 31(8), 1611-1622.
Bag, M., Gauri, S. K., & Chakraborty, S. (2012). An expert system for control chart pattern recognition. The International Journal of Advanced Manufacturing Technology, 62(1), 291-301.
Chowdhury, S., Mukherjee, A., & Chakraborti, S. (2015). Distribution‐free phase II CUSUM control chart for joint monitoring of location and scale. Quality and Reliability Engineering International, 31(1), 135-151.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Cuentas, S., Peñabaena-Niebles, R., & Garcia, E. (2017). Support vector machine in statistical process monitoring: a methodological and analytical review. The International Journal of Advanced Manufacturing Technology, 91(1-4), 485-500.
Demetgul, M. (2013). Fault diagnosis on production systems with support vector machine and decision trees algorithms. The International Journal of Advanced Manufacturing Technology, 1-12.
Ebrahimzadeh, A., Addeh, J., & Ranaee, V. (2013). Recognition of control chart patterns using an intelligent technique. Applied Soft Computing, 13(5), 2970-2980.
Electric, W. (1956). Statistical quality control handbook. Easton: The Mack Printing Company.
Epprecht, E. K., Aparisi, F., & Ruiz, O. (2018). Optimum variable-dimension EWMA chart for multivariate statistical process control. Quality Engineering, 30(2), 268-282.
Fergani, B. (2015). News schemes for activity recognition systems using PCA-WSVM, ICA-WSVM, and LDA-WSVM. Information, 6(3), 505-521.
Gani, W., & Limam, M. (2013). Performance Evaluation of One‐Class Classification‐based Control Charts through an Industrial Application. Quality and Reliability Engineering International, 29(6), 841-854.
Gauri, S. K., & Chakraborty, S. (2007). A study on the various features for effective control chart pattern recognition. The International Journal of Advanced Manufacturing Technology, 34(3), 385-398.
Gu, N., Cao, Z., Xie, L., Creighton, D., Tan, M., & Nahavandi, S. (2013). Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization. Journal of intelligent manufacturing, 24(6), 1241-1252.
Guh & Shih (2006). Improved neural network-based control chart pattern recognition using raw data and statistical data simultaneously. A Chinese Society for Quality, 2006.
Hachicha, W., & Ghorbel, A. (2012). A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Computers & Industrial Engineering, 63(1), 204-222.
Hassan, A., Baksh, M. S. N., Shaharoun, A. M., & Jamaluddin, H. (2003). Improved SPC chart pattern recognition using statistical features. International Journal of Production Research, 41(7), 1587-1603.
Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440.
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
Kao, L. J., Lee, T. S., & Lu, C. J. (2016). A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine. Journal of Intelligent Manufacturing, 27(3), 653-664.
Khormali, A., & Addeh, J. (2016). A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine. ISA transactions, 63, 256-264.
Li, T. F., Hu, S., Wei, Z. Y., & Liao, Z. Q. (2013). A framework for diagnosing the out-of-control signals in multivariate process using optimized support vector machines. Mathematical Problems in Engineering, 2013.
Lu, C. J., Shao, Y. E., & Li, C. C. (2014). Recognition of concurrent control chart patterns by integrating ICA and SVM. Applied Mathematics & Information Sciences, 8(2), 681
Mahdiyeh Eslami, Ali Noori & Moslem Amirinejad (2015). Control chart pattern recognition using neural networks and bees algorithm. ARSOM. 4(3), 97-105.
Montgomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).
Oviedo-Trespalacios, O., & Peñabaena-Niebles, R. (2016). Statistical performance of control charts with variable parameters for autocorrelated processes. Dyna, 83(197), 120-127
Pham, D. T., & Wani, M. A. (1997). Feature-based control chart pattern recognition. International Journal of production research, 35(7), 1875-1890.
Razzaghi, T., Roderick, O., Safro, I., & Marko, N. (2016). Multilevel weighted support vector machine for classification on healthcare data with missing values. PloS one, 11(5), e0155119.
Saleh, N. A., Mahmoud, M. A., Jones-Farmer, L. A., Zwetsloot, I. N. E. Z., & Woodall, W. H. (2015). Another look at the EWMA control chart with estimated parameters. Journal of Quality Technology, 47(4), 363-382.
Shaban, A., Shalaby, M., Abdelhafiez, E., & Youssef, A. S. (2010). Automated identification of basic control charts patterns using neural networks. Journal of Software Engineering and Applications, 3(03), 208.
Sung, J. (2001). CMP pad dresser: A diamond grid solution. In Proceedings of the Sixth Applied Diamond Conference/Second Frontier Carbon Technology Joint Conference(ADC/FCT 2001) (Vol. 1).
Sutanto, D. H., Ghani, A., & Khanapi, M. (2015). A Benchmark Feature Selection Framework for Non Communicable Disease Prediction Model. Advanced Science Letters, 21(10), 3409-3416.
Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281-287).
Wang, C. H., Guo, R. S., Chiang, M. H., & Wong, J. Y. (2008). Decision tree based control chart pattern recognition. International Journal of Production Research, 46(17), 4889-4901.
Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376.
Xanthopoulos, P., & Razzaghi, T. (2014). A weighted support vector machine method for control chart pattern recognition. Computers & Industrial Engineering, 70, 134-149.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2024-01-24起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2024-01-24起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw