運用資料探勘技術於分析生產良率之研究 -以矽晶圓產業的S公司為例

Abstract

矽晶圓是太陽能電池最重要的零件，從無到有需要較長的生產時間，生產過程有意外就無法趕不上客戶的交期，良率的好壞是影響獲取訂單的關鍵，因為找出影響良率好壞的品質分析方法就更重要。傳統的品質分析方法，如特性要因分析圖、柏拉圖、管制圖、六標準差等在製造業提升良率品質的運用已經行之有年，然而這種解析方法是依據經驗累積來增加效率，其作法並不穩定，過程也是缺乏效率。 資料探勘是從海量資料中發掘出隱藏於其中具有參考價值的資訊，可以提供給決策者做出決定的輔助，進而提昇生產良率品質。 本研究藉由MES系統擷取製程生產履歷記錄及所使用的生產參數及機台別，透過專業人士分析過濾出有用的生產屬性，去除無效的資料，透過資料增益的方法找出關鍵的生產屬性，分別再以決策樹C4.5、簡易貝式分類及支持向量機SVM三種分類演算法訓練資料、預測生產製程的良率，實際結果顯示支持向量機SVM表現最佳。

As the most important component of “Solar Cell”, the lead time of “Silicon Wafer” production is considerable. Any mistake happened during production process may fail to reach promised delivery time. Under this circumstance, production yield rate is the key to increase customer satisfaction and acquire more orders. Taking into account all these factors, it will be meaningful how to discover a quality analysis methodology which will impact production yield rate. Traditional methodologies, such as Cause-and-Effect diagram、Pareto Analysis、Control Charts、Six Sigma, are applied in manufacturing industry for quite a while, nevertheless all these paradigms were built up based on personnel experience which is not considered stable. Data mining, on the other hand, is a process to discover hidden relative information or specific models from tens of thousands of messages. The findings after data-mining process will then be a reference for decision makers to improve or stabilize production yield rate. Through MES system, this research aims to record all production histories, production parameters, and individual machine situation during a full-production cycle. After that, professionals will sift through abovementioned information and conclude key production drivers. Finally, Decision Tree C4.5, Naive Bayes, and SVM are applied respectively to foresee production yield rate. The performance of SVM is the best among the three classification models.