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。

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.

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