良率的高低是半導體產業競爭力代表的指標之一，除了新產品推出時間是否能搶先競爭者外，產品的良率是否能夠穩定的保持，更是奠定企業在產業的地位，在雷射元件生產的過程中，所有的生產製程資料會被完整的記錄在電腦整合製造資訊系統(CIM System)內。這些多維度且數量龐大的資料，若要以人工方法或是傳統統計方法進行整理分析，必須耗費較多的人力及時間。 本研究以個案公司的雷射晶粒及封裝製程過往生產資料為來源，並分為兩個階段進行資料採礦，第一個階段將晶粒製程中複雜度高的機台參數資料集進行資料前處理，將資料淨化成資料探勘的格式。第二個階段，結合晶粒製程關鍵機台參數值與封裝成品良率為資料來源，以個案公司制定之良率目標，將成品依據生產批良率目標值進行分類，再使用分類技術(Classification)中的決策樹(Decision tree)、類神經網路(Multi-Layer Perceptron)、支援向量機(SVM)…等演算法進行資料探勘，評估各演算法中製程機台參數與成品良率採礦後結果，最後將分析結果提供於製程人員進行確認及評估。 本研究目標主要為找出晶粒製程關鍵及隱含的參數與雷射封裝成品良率間關係，進而提供製程人員進行產品製程基礎規範參考依據。經過本次實驗後發現，在半導體雷射製程確實能透過資料探勘的技術，挖掘出製程中有控管的關鍵參數及影響良率卻未進行控管的製程機台參數。而實驗結果中以多重分類器Boosting 的CART演算法表現最好。 ;Yield rate, or defect-free rate, is one of many indexes to evaluate semiconductor manufacturers’ competitiveness. Manufacturers not only strive to introduce new products to the market earlier than their competitors, but also have to maintain a steady yield rate so as to ensure their competitive edge in the industry. All data related to the laser component manufacturing process is recorded in a CIM system. However, the multidimensional data is too numerous to be counted. Lots of time and manpower would be consumed if data is classified and analyzed manually or using the conventional statistic methods. Upon receiving the laser chip-process and packaging-process data from a chosen corporation, the author started to mine data in two phases. In Phase 1, highly complicated machine parameters were obtained from the crystal particle manufacturing process for data preprocessing and thus data was converted into the data-mining format. In Phase 2, several crystal particles’ key machine parameters and packaged finished products’ yield rates were integrated into the data; the finished products were classified using the yield rate and the batch yield rate specified by the chosen corporation. After that, various classification techniques such as decision tree, neural network, and Support Vector Machine (SVM) are used to evaluate the machine parameters and to assess the results of finished products’ yield rates obtained from data-mining. Lastly, the analysis results were presented to process technicians for validation and evaluation. The author attempted to identify the relationship between the key parameters as well as the implicit parameters related to crystal particle manufacturing process and thereby presented the basic specifications for process technicians’ reference. Most importantly, the experimental results indicate that the semiconductor laser process itself was sufficient to identify the key parameters of the manufacturing process as well as the machine parameters that affect the yield rate but they are not controlled at this moment. The results also indicate that CART, when boosted by multiple classifiers, is the most effective algorithm.
