資訊管理學報
陳暎仁;簡禎富;黃馨滿;
頁: 129-153
日期: 2016/04
摘要: 彩色濾光膜及微透鏡為製造CMOS影像感測器的關鍵製程，為了提昇產品良率，必須在製造過程中找出造成缺陷樣型的可能原因，以進行修復、減少重工。目前彩色濾光膜廠多半憑藉工程師的領域知識與經驗法則來做故障排除，然而這種方式很容易因為經驗不足而誤判，同時不夠快速且準確度不高。本研究根據實際需求發展一套彩色濾光膜及微透鏡資料挖礦架構模式，以協助工程師診斷造成缺陷樣型的原因，具體步驟包括蒐集影像感測元件彩色濾光膜廠的缺陷樣型相關 資料及資料預處理、透過列聯表分析進行卡方獨立性檢定與Cramer’s V相關係數 分析個別變數之相關性、透過隨機重複切割方式與Apriori演算法產生關聯規則並利用測試集資料進行規則篩選，最後統合列聯表分析以及關聯規則模型所產生的顯著規則，以支持度、信賴度與增益三個指標進行規則評估。本研究以新竹科學園區某影像感測元件彩色濾光膜廠進行實證以檢驗效度，結果顯示本研究所提出之資料挖礦架構能夠有效的輔助工程師進行缺陷分析，達到故障排除與良率提昇之效。
關鍵字: 彩色濾光膜及微透鏡製程;缺陷樣型;資料挖礦;大數據分析;關聯規則;製造智慧;工業3.5;
Abstract: Purpose-CMOS image sensor (CIS) manufacturers usually rely on domain knowledge for troubleshooting, yet it may not be effective nor efficient due to lack of experience and increasing complexity. Focusing on realistic needs, this study aims to develop a framework based on data mining and big data analytics for analyzing defect patterns in color filter and microlens to enhance yield. Design/methodology/approach - The proposed framework integrates data collection and preprocessing of defect patterns, univariate correlation analysis (by Chi-square independence test and Cramer’s V correlation coefficient), and association rules (generated by Apriori algorithm via splitting dataset into training and testing repeatedly). The derived rules were evaluated by the indices of support, confidence, and lift. Findings-An empirical study was conducted in a leading CIS manufacturing company in Taiwan to validate the proposed approach. The results have shown the practical viability of the proposed approach to effectively and efficiently resolve the present problem. Research limitations/implications-This study focused only on three data types (including process tools, recipes, and process time) to generate rules to support troubleshooting. Future research could incorporate inline data such as metrology and equipment parameters to construct the model for further investigation. Practical implications-The color filter and microlens processes are critical for CMOS image sensor manufacturing. It is crucial to identify possible root causes of defect patterns to troubleshoot and enhance product yield and reduce the loss of reworks in short time. The proposed framework provides a systematic approach to identify the root causes of specific defect patterns effectively and efficiently. Originality/value-This study firstly employ data mining and big data analytics for troubleshooting and yield enhancement of CIS manufacturing and developed an effective solution.
Keywords: color filter and microlens process;defect pattern;data mining, big data analytics;association rules;manufacturing intelligence;industry 3.5;
瀏覽次數: 22617 下載次數: 5375
引用 導入Endnote
頁: 129-153
日期: 2016/04
摘要: 彩色濾光膜及微透鏡為製造CMOS影像感測器的關鍵製程，為了提昇產品良率，必須在製造過程中找出造成缺陷樣型的可能原因，以進行修復、減少重工。目前彩色濾光膜廠多半憑藉工程師的領域知識與經驗法則來做故障排除，然而這種方式很容易因為經驗不足而誤判，同時不夠快速且準確度不高。本研究根據實際需求發展一套彩色濾光膜及微透鏡資料挖礦架構模式，以協助工程師診斷造成缺陷樣型的原因，具體步驟包括蒐集影像感測元件彩色濾光膜廠的缺陷樣型相關 資料及資料預處理、透過列聯表分析進行卡方獨立性檢定與Cramer’s V相關係數 分析個別變數之相關性、透過隨機重複切割方式與Apriori演算法產生關聯規則並利用測試集資料進行規則篩選，最後統合列聯表分析以及關聯規則模型所產生的顯著規則，以支持度、信賴度與增益三個指標進行規則評估。本研究以新竹科學園區某影像感測元件彩色濾光膜廠進行實證以檢驗效度，結果顯示本研究所提出之資料挖礦架構能夠有效的輔助工程師進行缺陷分析，達到故障排除與良率提昇之效。
關鍵字: 彩色濾光膜及微透鏡製程;缺陷樣型;資料挖礦;大數據分析;關聯規則;製造智慧;工業3.5;
Constructing a Data Mining Framework for Analyzing Defect Patterns of Color Filter and Microlens
Abstract: Purpose-CMOS image sensor (CIS) manufacturers usually rely on domain knowledge for troubleshooting, yet it may not be effective nor efficient due to lack of experience and increasing complexity. Focusing on realistic needs, this study aims to develop a framework based on data mining and big data analytics for analyzing defect patterns in color filter and microlens to enhance yield. Design/methodology/approach - The proposed framework integrates data collection and preprocessing of defect patterns, univariate correlation analysis (by Chi-square independence test and Cramer’s V correlation coefficient), and association rules (generated by Apriori algorithm via splitting dataset into training and testing repeatedly). The derived rules were evaluated by the indices of support, confidence, and lift. Findings-An empirical study was conducted in a leading CIS manufacturing company in Taiwan to validate the proposed approach. The results have shown the practical viability of the proposed approach to effectively and efficiently resolve the present problem. Research limitations/implications-This study focused only on three data types (including process tools, recipes, and process time) to generate rules to support troubleshooting. Future research could incorporate inline data such as metrology and equipment parameters to construct the model for further investigation. Practical implications-The color filter and microlens processes are critical for CMOS image sensor manufacturing. It is crucial to identify possible root causes of defect patterns to troubleshoot and enhance product yield and reduce the loss of reworks in short time. The proposed framework provides a systematic approach to identify the root causes of specific defect patterns effectively and efficiently. Originality/value-This study firstly employ data mining and big data analytics for troubleshooting and yield enhancement of CIS manufacturing and developed an effective solution.
Keywords: color filter and microlens process;defect pattern;data mining, big data analytics;association rules;manufacturing intelligence;industry 3.5;
瀏覽次數: 22617 下載次數: 5375
引用 導入Endnote