用於監控製程之管制圖上所出現的異常模式常會和某些造成製程失控的特定原因有所相關，因此研究人員長期以來一直在尋求可以找出這些異常模式特徵的方法，過往的方法大多數是使用分類器來根據特定的類型標記出異常管制圖模式，但是長期的管制圖數據通常包含大量局部性的異常模式，其特性可能不同於在長期管控圖全局視野下d可觀察到的特徵，這些局部異常模式也很有可能有值得分析的價值。在本篇論文中，我們提出了一種新的管控圖識別方案，該方案不專注於單個管制圖的數據分類，而是將基於直方圖的數據表示法與時間序列的子序列匹配演算法相結合，以從長期的管制圖數據中識別各種規模(尺度)的異常模式，實驗結果證明了所提出框架的有效性，可以有效檢測各種規模的管制圖模式，其性能優於現存表現最為優異的幾個子序列匹配算法。

Abnormal patterns in the control charts used for manufacturing can be linked to assignable causes, and researchers have long sought to develop methods by which to characterize those patterns. Most previous methods used a classifier to label abnormal control chart patterns according to specific types. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis.
Besides classifying short-term control chart patterns, labeling the long period chart based on different time scales is also an important task in semiconductor manufacturing industry. This paper presents a novel control chart pattern recognition scheme which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses histogram-based data representation in conjunction with time-series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time-series subsequence matching algorithms.

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