近幾年來半導體技術快速的發展，如何有效控制製程的變異及找出發生製程變異的原因，對其良率提升具有決定性的影響。而當製程發生變異時，最直接的偵測方式之一即是分析晶圓的缺陷圖樣，並藉由缺陷圖樣追蹤可能發生的製程異常，過去在缺陷圖樣這方面的研究，主要是從兩大方向來進行：統計分析及人工智慧的方式。統計分析的方法可有效地偵測出晶圓上可能發生聚集的現象，但是必須在某一假設分配下，來判定聚集現象的類型，也因此限制了缺陷圖樣辨識的應用性。就人工智慧方法而言，則可藉由收集具代表性的缺陷圖樣來進行學習，以進行缺陷圖樣的辨識，然而缺陷圖樣的變異性大，當從晶方或是缺陷層面直接設計訓練樣本，若無法有效控制缺陷圖樣在方向及位置上的差異，以及當產品設計變動時，訓練樣本必須重新設計，因此限制此法在應用上的效果。有鑑於此，本研究提出利用區域分割的概念發展缺陷圖樣分析架構，以分析晶圓上的缺陷圖樣，此方法主要分為四大部分：一是設計影像處理遮罩，定義晶圓上缺陷的位置及大小，第二部份利用區域分割及最小矩形區域概念，定義晶圓上的缺陷群聚區域，三是建構模糊推論系統來呈現晶圓陷圖樣的，第四部份利用信號處理的特性，來輔助進行晶圓缺陷的辨識。本研究進行測試時，以國內半導體廠商所提供的缺陷圖樣測試資料為參考，目前研究成果顯示，本研究所提出的晶圓缺陷圖樣分析架構，可定義出晶圓上的環狀、刮傷、區塊聚集型及重複性的缺陷圖樣。

ABSTRACT

Process variation control and root causes elimination are critical to the issue of yield enhancement in semiconductor manufacturing industry. To detect the existence of process variation, one of the most effective ways is to analyze the spatial defect patterns exhibiting on the wafers. Many research works have been proposed to help recognize the spatial patterns. These works can basically be classified into statistical approach and training based approach. In statistical approach, the defect data are statistically analyzed to find out the clustering phenomena, and it usually cannot conduct further analysis to identify the specific defect patterns under certain statistical hypothesis. Comparing to the statistical approach, the training based approach has the capability of classifying different spatial defect patterns. But it requires the collection of enough training samples, which is usually very time-consuming. Moreover, when the product or process changes, the training process needs to be executed again. For these reasons, this research developed a Sub-Region based pattern recognition algorithm to identify and classify ring, scratch, zone, and repeating type defect map patterns such that human intervention can be replaced. The presented analysis architecture comprises of four modules: defect localization and noise reduction, defect clusters representation by Sub-Region and Minimum Rectangle Area (MRA), defect map pattern identification, and defect map pattern recognition. The experimental results show that the presented approach can achieve the intended purposes.

李偉傑，「半導體之工程資料分析與診斷系統」，碩士論文，國立清華大學工業工程與工程管理學系研究所（1995）。
林寅智，「以工程資料為基礎之半導體故障分析系統」，碩士論文，國立清華大學工業工程與工程管理學系研究所（1997）。
莊達人，VLSI製造技術，高立圖書有限公司，1997年。
劉淑範，「以工程資料為基礎之半導體良率提升分析系統」，碩士論文，國立清華大學工業工程與工程管理學系研究所（1997）。
林景堂，「晶圓圖像辨識」，碩士論文，國立台灣大學資訊工程學系研究所（1998）。

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