- 學位論文
整合虛擬量測系統與大數據之預測模式建置－以半導體業為例
The Integration of Virtual Metrology and Big Data for Constructing the Prediction Model ─ Case Study in Semiconductor Industry
摘要
本論文以工業4.0和大數據為研究背景，藉由半導體產業中的趨勢運用虛擬量測系統（Virtual Metrology, VM），以解決量測延遲的問題並使得該系統俱備即時性和準確性，進而使企業達到提升品質以及生產效率的目標。 模式建立主要使用主成份分析及迴歸分析來進行重要製程參數因子之篩選，預測方法主要使用類神經網路，模式的驗證個案從半導體製程中的低壓化學氣相沉積（Low Pressure Chemical Vapor Deposition, LPCVD）設備取得，總共製程參數為18種且資料筆數為264筆，經篩選重要參數因子後以其中的200筆資料進行網路訓練，64筆資料進行驗證，結果將預測值和實際值兩相比較。模式的評估指標是用平均絕對百分比誤差（Mean Absolute Percentage Error, MAPE）來對預測系統做評判，而整體虛擬量測系統的預測結果小於10%屬於高準確性的預測。
並列摘要
Under Industry 4.0 concept and the management of big data issues in dealing for rapid decision making for improved productivity, the application development of Virtual Metrology in semiconductor industry for the metrology delay problem was investigated to make the system that becomes more immediacy and accuracy that increase quality and productivity for product/process. The predicted model which is a combination of the methodologies of Principal Component Analysis and Regression Analysis was proposed to filter the key factors of process. Addition to, the Artificial Neural Network are carried out to identify specific problems which are evaluated by Mean Absolute Percentage Error. Further on, a case study in Low pressure chemical vapor deposition equipment of process of semiconductor based on the 18 parameters and 264 data is discussed. The result of this system was proven to have good performance that the MAPE less than 10 percent.