交錯圖形偵測應用於半導體缺陷圖樣辨識之研究

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研究生：	王怡絜 Wang, I-Chiech
論文名稱：	交錯圖形偵測應用於半導體缺陷圖樣辨識之研究 Detection and Identification of Intersecting Defect Patterns in Semiconductor Manufacturing
指導教授：	陳飛龍
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	84
中文關鍵詞：	半導體、良率、缺陷分析、影像處理
外文關鍵詞：	Semiconductor, Yield, Defect analysis, Image processing
相關次數：	點閱：3 下載：0
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半導體的製造過程極為複雜，因此良率的提升成為一項非常具有挑戰性的議題。提昇良率最直接的方法則是專注在製造過程，即時的發現異常情況。透過空間缺陷辨識可以迅速發現線上運作問題。簡言之，偵測以及辨識空間缺陷圖形可以發現問題並且得知問題起因，使工程師可以迅速修復避免良率的損失。
過去的研究中，學者發展出許多方法得以將缺陷圖進行分類辨識，但卻只著重在單一種缺陷型態，而忽略了更具複雜性的交錯圖形。因此，本研究著重在發展一套方法解決交錯型的缺陷圖形，以幫助工程師即時找到問題癥結點。本研究的目的包括： (1) 發展一套方法解決交錯缺陷圖形，包含線性交錯以及弧形交錯， (2) 比較不同的方法對於交錯圖形的辨識表現， (3) 建構完整的系統解決交錯圖形。
本研究之方法透過模擬資料測試以及業界知名半導體公司所提供之缺陷圖測試其績效。透過實證性的測試，證實本研究可以正確辨識出交錯型缺陷圖形，相較於人工辨識時間，本研究方法可以有效的縮短缺陷圖形辨識的時間。

The processes of semiconductor manufacturing have become more complicated and its yield faces a big challenge. Finding the way of improving the yield is a very critical issue. The direct method to enhance the yield should focus on the manufacturing processes and detecting the unusual situations as soon as possible. Spatial defect recognition can discover the problems of manufacturing processes directly. Defect detection and recognition can trace the problems and find out root cause such that engineers can modify it timely to avoid yield loss.
A lot of research works about defects classification have been discussed in the past, but the studied defect patterns are usually single patterns. The researchers ignored the intersecting patterns which are usually much more complicated. Therefore, this research propose to develop an algorithm to deal with the intersecting patterns for helping engineers find out the root cause in the manufacturing rapidly. The objectives of this research are (1) to develop the methodologies to deal with the intersecting patterns including line-shaped cross and curve-shaped cross, (2) to evaluate the performance of different methodologies for intersecting types, and (3) to enhance the performance and save the processing time.
The developed methodology has been verified with real data collected from a famous semiconductor company. The experimental results demonstrate that the proposed methodology can not only has high accuracy but also save much time on dealing with the defect identification comparing to human operations.

CONTENTS
ABSTRACT    I
ABSTRACT(CHINESE)    II
ACKNOWLEDGEMENT    III
CONTENTS    IV
LIST OF TABLES    VI
LIST OF FIGURES    VII
CHAPTER 1 INTRODUCTION    1
1.1 RESEARCH BACKGROUND    1
1.2 RESEARCH MOTIVATION    2
1.3 RESEARCH OBJECTIVES    4
1.4 RESEARCH METHODOLOGY    5
1.5 FRAMEWORK OF THESIS    9
CHAPTER 2 LITERATURE REVIEW    10
2.1 INTRODUCTION OF SEMICONDUCTOR MANUFACTURING    10
2.1.1 The Procedure of Semiconductor Manufacturing    10
2.1.2 The Definition of Particles, Defects, and Faults    15
2.1.3 The Classification of Defects    16
2.2 DIGITAL IMAGE PROCESSING    18
2.2.1 The Concept of Imaging Processing    18
2.2.2 The Pre-Processing in Imaging Processing    19
2.2.3 Image Segmentation    21
2.2.4 Feature Extraction    26
2.2.5 Representation and Description    27
2.3 INTRODUCTION OF CLUSTERING METHODOLOGY    30
2.4 FUZZY CLUSTERING METHODOLOGY    35
2.5 GRAPH REPRESENTATION    39
CHAPTER 3 DEVELOPMENT OF THE DETECTION AND IDENTIFICATION OF INTERSECTING DEFECT PATTERNS    42
3.1 ARCHITECTURE OF THE PROPOSED ALGORITHM    42
3.1.1 The definition of problems    42
3.1.2 Architecture of the proposed algorithm    44
3.2 THE DETECTION AND IDENTIFICATION OF INTERSECTING DEFECT PATTERNS ALGORITHM    47
3.2.1 The filtering of defects    47
3.2.2 The analysis of defects    52
3.2.3 The extraction of features    56
3.3 THE STRUCTURE OF KNOWLEDGE BASE    59
CHAPTER 4 SYSTEM IMPLEMENTATION AND VERIFICATION    61
4.1 STRUCTURE OF THE DEVELOPED SYSTEM    61
4.2 INTRODUCTION OF THE DEVELOPED SYSTEM    63
4.2.1 Defect loader function    64
4.2.2 Defect filter function    65
4.2.3 Defect detector function    66
4.2.4 Defect identification function    67
4.3 VERIFICATION OF SYSTEM    69
4.3.1 Performance of identifying clustered defects    69
4.3.2 Processing time of system    75
CHAPTER 5 CONCLUSION AND FURTHER RESEARCH    78
5.1 SUMMARY    78
5.2 FURTHER INVESTIGATION    80
REFERENCES    81


LIST OF TABLES
Table 3.1 The results of test samples after filtering.    51
Table 3.2 The results of test samples after clustering based on distance concept.    52
Table 3.3 The results of test samples after segmenting based on detection defect patterns algorithm.    55
Table 3.4 The skeletons of clusters.    56
Table 3.5 Features versus pattern types.    58
Table 3.6 Contents of member functions.    59
Table 3.7 Contents of if-then rule.    60
Table 4.1 The description of aid functions.    64
Table 4.2 The line samples.    70
Table 4.3 The curve samples.    72
Table 4.4 The experimental results of line samples.    74
Table 4.5 The experimental results curve samples.    74
Table 4.6 The accuracy of results.    75
Table 4.7 The processing time of line samples    76
Table 4.8 The processing time of curve samples    76
Table 4.9 The average processing time of all samples    76


LIST OF FIGURES
Figure 1.1 The Framework of Defect Analysis    5
Figure 1.2 The four vertices and analysis of intersecting area.    7
Figure 2.1 The Chip-Making Process. [www.infras.com]    11
Figure 2.2 (a) Random defects, evenly distributed. (b) Systematic, reticle induced defects. (c) Systematic defects, edge effects. (d) Hybrid defects. [Ulrich, 1995]    17
Figure 2.3 Fundamental steps in digital image processing.[Gonzalez, 2002]    18
Figure 2.4 3×3 smoothing filter mask.    20
Figure 2.5 (a) the origin image has noise (b) after median filter, the isolate has be removable.    20
Figure 2.6 The process of image analysis.    21
Figure 2.7 3×3 mask.    22
Figure 2.8 (a) point detection mask, (b) horizontal line mask, (c) +45° line mask, (d) vertical line mask (e) -45° line mask [Rafael & Richard 1993]    23
Figure 2.9 (a) Sobel operator to compute the mask of  ,  (b) Sobel operator to compute the mask of  .    23
Figure 2.10 Laplacian mask.    24
Figure 2.11 The split and merge region-based segmentation.    25
Figure 2.12 The direction of object.    27
Figure 2.13 The square of object with direction and not.    27
Figure 2.14 The signature of ring.    28
Figure 2.15 (a) 4-directional chain codes (b) 8-directional chain codes.    28
Figure 2.16 (a) Original (b) 4-directional chain codes (c) 8-directional chain codes.    29
Figure 2.17 Polygonal representation of object    29
Figure 2.18 Intersecting pattern (a) data set, (b) partition obtained by the fuzzy k-means algorithm has not reconstructed the two input data, (c) the FKR correctly identified the two data.    38
Figure 2.19 Concentric pattern (a) data set, (b) partition obtained by fuzzy k-means algorithm did not recover the shape of the input patterns, (c) detection of the shape of the input patterns by FKR.    38
Figure 2.20 Examples of symbols in graph representation. Graph nodes (black) and graph edges (white). The intersection node joins graph edges belonging to the line and graph edges belonging to the symbol. [Boatto, 1992]    40
Figure 2.21 Results of symbol separation. Those parts of the intersection area that are discarded after the cut are shown in black.    41
Figure 3.1 The generally recognition of pattern type.    43
Figure 3.2 The defined types of crossover patterns.    44
Figure 3.3 The Framework of Defect Analysis    45
Figure 3.4 The steps of defect recognition.    46
Figure 3.5 The coefficient of the 5×5mask    48
Figure 3.6 The procedure of defect filtering    49
Figure 3.7 (a) isolated defect, (b) non-isolated defect.    50
Figure 3.8 (a) defect map, (b) centralize defects, (c) random defects.    51
Figure 4.1 The structure of the developed system.    62
Figure 4.2 The main frame of the developed system.    63
Figure 4.3 The function of defect loader:(a) program frame, (b) the report frame.    65
Figure 4.4 The function of defect filter: (a) the program frame, (b) the report frame.    66
Figure 4.5 The function of defect detector: (a) the program frame, (b) the report frame.    67
Figure 4.6 The function of defect identification: (a) the program frame, (b) the report frame.    68
Figure 4.7 (a) The comparison between defects and processing time of line samples, (b) The comparison between defects and processing time of curve samples.    77

                                

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