| 摘要: | 在最近的國際半導體技術藍圖中，測試與自動測試設備報告指出艱難的挑戰之一是偵測系統性的瑕疵；良率強化報告同時也指出未來在特徵化，檢測和分析的最重要的關鍵挑戰是非可視性瑕疵和製程變動的識別。本計畫“量產晶圓圖的非隨機瑕疵識別”的目標是從晶圓圖分析中，判別是否有非隨機瑕疵的樣態，其中包括過度群聚與反群聚，並用一個特徵值來表示具有隨機性質的瑕疵樣態，進而能從量產晶圓圖中，利用單晶圓的分割、區塊大小的調整以及多晶圓的重疊解析出更多樣的瑕疵樣態。隨機瑕疵的樣態是什麼?依統計假設檢定的方法，吾人以完全空間隨機性瑕疵晶圓圖的瑕疵樣態為虛無假設，比較待檢驗晶圓圖，如果數據集之間的關係小於閾值概率（顯著性水平），則認為不太可能實現虛無假設--具有統計顯著性。所以要判別是否有非隨機瑕疵的樣態(對立假設)，須要從探索完全空間隨機性瑕疵晶圓圖的瑕疵樣態著手開始。本計畫包括三個項目進行: (一)推導一個強健的非隨機檢定，吾人要公式化這個檢定的模型，直接表達成和晶圓良率、晶粒大小以及信心水準的式子。(二)尋求一個特徵值，用來表示瑕疵樣態的隨機性，晶圓良率雖是不錯的選擇，但它還不太能完全表達瑕疵分佈的隨機性，吾人要組合出一個新的特徵數，當晶圓圖瑕疵分佈是隨機的，這特徵數會近似平均瑕疵數--每個晶粒上瑕疵個數，特徵數偏小表示晶圓圖有過度群聚現象，偏大表示有反群聚現象。以及(三)資料探勘，從量產晶圓圖中，解析出更多樣的瑕疵樣態和等效瑕疵(非可視性瑕疵和製程變動)的綜合效應。 ;In a recent International Technology Roadmap for Semiconductors (ITRS), the Test and Automatic Test Equipment reports that one of the difficulties of the challenge is to detect systemic defects. In Yield Enhancement, this report also pointed out that the identification of Non-Visual Defects and Process Variations was set to the most important key challenge in the future. The project "Identification of the Nonrandom Defects in Volume Production Wafer Maps" aims to determine whether there is a non-random defect pattern from the wafer map analysis, including over-clustering and anti-clustering. The number of defects is used to represent the random nature, and then from the mass production wafer maps, the use of single-wafer segmentation, block size adjustment and multi-wafer overlap to resolve more defect Pattern. What is the pattern of random defects? According to the method of statistic hypothesis test, the defect pattern of the complete spatially random defect wafer map is assumed to be null hypothesis, and the defect pattern of the wafer map to be inspected is compared. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Therefore, to determine whether there is a non-random defect pattern (the alternative hypothesis), we must start from exploring the defect pattern of complete space random defect wafer map.The project consists of three topics: (a) “Deriving a robust nonrandom test”, I have to formulate the test model to express directly with wafer yield, die size and confidence level. (B) “Finding a representative”, wafer yield is a good choice, but it is not fully able to express the homogeneous defect distribution. A new number of features is formed. When the defect pattern of the wafer map is random, the number of features will approximate equal the average number of defects - the number of defects on each die. The smaller the number shows an over-clustering phenomenon, and larger that there is an anti-clustering phenomenon. And (c) “Data mining”, from the production of wafer maps, resolve a more diverse defect patterns and the combined effect of equivalent defects (non-visual defects and process variations). |
