Analysis of Defective Patterns on Wafers
in Semiconductor Manufacturing:
A Bibliographical Review
1Bong-Jin
Yum, 2Jae Hoon Koo, and 3Seong-Jun Kim
1Department
of Industrial and Systems Engineering,
KAIST, Republic of Korea
2Quality Division, Hyundai Mobis Co., Ltd., Republic of Korea
3Department of Industrial, Information, and Management Engineering,
Gangneung∙Wonju National University, Republic of Korea
I. Introduction
• Yield Management: One of the most important activities in
semiconductor manufacturing.
• Stage of Interest: Electrical test stage after fabrication
– Semiconductor Manufacturing Process
• Analysis Unit: Binary (i.e., good or defective) wafer bin maps
(WBMs).
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
I. Introduction
• Patterns of Defective Dies on a Wafer
–
Random vs Local defect
• Random defect: Randomly located and occurred by common causes
• Local defect: Nonrandomly located and occurred by assignable causes
– Assignable causes for patterns of local defects
Local defect pattern
Assinable causes
Linear scratch
Machine handling problem
Elliptical zone
Thin film deposition process
Ring
Etching process problem
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
I. Introduction
• Process of Defect Pattern Analysis
Start
Wafer bin maps
No dependence
Spatial
randomness
test
Automatic defect detection
Dependence
Defect pattern
classification
Automatic defect classification
Root cause analysis
End
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
I. Introduction
• Detection and Classification of Cluster Patterns on a Wafer:
– Important for identifying assignable causes and taking actions
for yield enhancement.
– Need to be automated for improved productivity and accuracy.
– Enable to improve the effectiveness of process control by fast
feedback.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
I. Introduction
• Purpose of Paper: Provide a bibliographical review of the
literature on automatic detection (AD) and/or classification
(ADC) of clusters of defective dies using WBMs.
• The present review include the existing works on:
– Spatial randomness test (SRT)
– Automatic detection of clusters
– Automatic detection and classification of clusters
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
A. Spatial Randomness Test
– Statistical dependence test between data points
– Most of SRTs are based on Join-Count (JC) statistics [40].
• Log odds ratio: [2], [3], [6]
• Generalized JC statistics: [24]
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
A. Spatial Randomness Test
– Statistical dependence test between data points
– Most of SRTs are based on Join-Count (JC) statistics [40].
•
Log odds ratio: [2], [3], [6]
LOR  log
c00c11
 c01 2
2
c00 : defective-defective dies join
c01 : defective-good dies join
c11 : good-good dies join
[2] M. H. Hansen, V. N. Nair, and D. J. Friedman, “Monitoring wafer map data
from integrated circuit fabrication processes for spatially clustered defects,”
Technometrics, vol. 39, no. 3, pp. 241-253, 1997.
[3] C. K. Hansen and P. Thyregod, “Use of wafer maps in integrated circuit
manufacturing,” Microelectronics Reliability, vol. 38, pp. 1155-1164, 1998.
[6] W. Taam and M. Hamada, “Detecting spatial effects from factorial experiments:
An application from integrated-circuit manufacturing,” Technometrics, vol. 35, no.
2, pp. 149-160, 1993.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
A. Spatial Randomness Test
– Statistical dependence test between data points
– Most of SRTs are based on Join-Count (JC) statistics [40].
•
•
Log odds ratio: [2], [3], [6]
Generalized JC statistics: [24]
 [24] Y. S. Jeong, S. J. Kim, and M. K. Jeong, “Automatic identification of
defect patterns in semiconductor wafer maps using spatial correlogram
and dynamic time warping,” IEEE Transactions on Semiconductor
Manufacturing, vol. 21, no. 4, pp. 625-637, 2008.
 Considered expended join distance
 More accurate than normal JC statistics
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
B. Automatic detection of defective dies
– Stage of determining the existence of local defective dies on a
wafer
– The existing works focused on various spatial randomness test for
each wafer. They include the following.
•
•
•
•
Grey-scale level: [8]
Median filter & nearest-neighbor method: [10]
Image processing algorithm: [12]
SRT & invariant transformation distance: [13]
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
B. Automatic detection of defective dies
– Stage of determining existence of local defective dies on a wafer
– The existing works focused on various spatial randomness test for
each wafer. They include the following.
•
•
•
Grey-scale level: [8]
 [8] D. J. Friedman, M. H. Hansen, V. N. Nair, and D. A. James, “Model-free
estimation of defect clustering in integrated circuit fabrication,” IEEE
Transactions on Semiconductor Manufacturing, vol. 10, no. 3, pp.344-359, 1997.
Median filter & nearest-neighbor method: [10]
 [10] C. J. Huang, C. F. Wu, and C. C. Wang, “Image processing techniques for
wafer defect cluster identification,” IEEE Design & Test of Computers, vol. 19,
no. 2, pp.44-48, 2002.
Image processing algorithm: [12]
 [12] W. J. Tee, M. P. L. Ooi, Y. C. Kuang, and C. Chan, “Defect cluster
segmentation for CMOS fabricated wafers,” in 2009 Conference on Innovative
Technologies in Intelligent Systems & Industrial Applications, Malaysia, pp. 134138.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
B. Automatic detection of defective dies
– Stage of determining existence of local defective dies on a wafer
– The existing works focused on various spatial randomness test for
each wafer. They include the following.
•
•
•
•
Grey-scale level: [8]
Median filter & nearest-neighbor method: [10]
Image processing algorithm: [12]
SRT & invariant transformation distance: [13]
 [13] M. C. Weng, “Classification of wafer bin maps by using spatial tests and
invariant transformation distance,” M.S. thesis, Department of Statistics, National
Cheng Kung University, Tainan City, Taiwan, 2008.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
C. Automatic detection and classification of defective dies
– AD and determination of patterns of cluster of local defective dies
– The existing works focused on mixture of data mining methods.
•
•
•
•
•
Clustering & decision tree: [31], [32], [33] , [35]
Clustering & Hough transformation: [26], [36]
Unsupervised neural network: [16], [18]
Correlogram & 1-nearest neighborhood classifier: [24]
Wavelet transformation & neural network: [28]
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
C. Automatic detection and classification of defective dies
– AD and determination of patterns of cluster of local defective dies
– The existing works focused on mixture of data mining methods.
•
Clustering & decision tree: [31], [32], [33] , [35]
 [31] C. H. Wang, “Recognition of semiconductor defect patterns using spectral
clustering,” in 2007 IEEE International Conference on Industrial Engineering and
Engineering Management, Singapore, pp. 587-591.
 [32] C. H. Wang, “Recognition of semiconductor defect patterns using spatial
filtering and spectral clustering,” Expert Systems with Applications, vol. 34, no. 3,
pp. 1914-1923, 2008.
 [33] C. H. Wang, “Separation of composite defect patterns on wafer bin map
using support vector clustering,” International Journal of Production Economics,
vol. 36, no. 2, pp. 2554-2561, 2009.
 [35] C. H. Wang, S. J. Wang, and W. D. Lee, “Automatic identification of spatial
defect patterns for semiconductor manufacturing,” International Journal of
Production Research, vol. 44, no. 23, pp. 5169-5185, 2006.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
C. Automatic detection and classification of defective dies
– AD and determination of patterns of cluster of local defective dies
– The existing works focused on mixture of data mining methods.
•
•
•
Clustering & decision tree: [31], [32], [33] , [35]
Clustering & Hough transformation: [26], [36]
 [26] B. Kundu, K. P. White Jr., and C. Mastrangelo, “Defect clustering and
classification for semiconductor devices,” in 2002 Midwest Symposium on
Circuits and Systems, pp. II-561–II-564.
 [36] K. P. White, B. Kundu, and C. M. Mastrangelo, “Classification of defect
clusters on semiconductor wafers via the Hough transformation,” IEEE
Transactions on Semiconductor Manufacturing, vol. 21, no. 2, pp. 272-278, 2008.
Correlogram & 1-nearest neighborhood classifier: [24]
 [24] Y. S. Jeong, S. J. Kim, and M. K. Jeong, “Automatic identification of defect
patterns in semiconductor wafer maps using spatial correlogram and dynamic time
warping,” IEEE Transactions on Semiconductor Manufacturing, vol. 21, no. 4, pp.
625-637, 2008.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
C. Automatic detection and classification of defective dies
– AD and determination of patterns of cluster of local defective dies
– The existing works focused on mixture of data mining methods.
•
•
•
•
Clustering & decision tree: [31], [32], [33] , [35]
Clustering & Hough transformation : [26], [36]
Correlogram & 1-nearest neighborhood classifier: [24]
Unsupervised neural network: [16], [18]
 [16] F. L. Chen and S. F. Liu, “A neural-network approach to recognize defect
spatial pattern in semiconductor fabrication,” IEEE Transactions on Semiconductor
Manufacturing, vol. 13, no. 3, pp. 366-373, 2000.
 [18] F. Di Palma, G. De Nicolao, O.M. Donzelli, and G.Miraglia, “Unsupervised
algorithms for the automatic classification of EWS maps: A comparison,” in 2005
IEEE International Symposium on Semiconductor Manufacturing, San Jose,
California, pp. 253-256.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
II. Bibliography
C. Automatic detection and classification of defective dies
– AD and determination of patterns of cluster of local defective dies
– The existing works focused on mixture of data mining methods.
•
•
•
•
•
Clustering & decision tree: [31], [32], [33] , [35]
Clustering & Hough transformation: [26], [36]
Correlogram & 1-nearest neighborhood classifier: [24]
Unsupervised neural network: [16], [18]
Wavelet transformation & supervised neural network: [28]
 [28] S. F. Liu, F. L. Chen, and, A. S. Chung, “Using wavelet transform and neural
network approach to develop a wafer bin map pattern recognition model,” in 2008
Proceedings of the International MultiConference of Engineers and Computer
Scientists, Hong Kong, 4 pages.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
III. Discussions
• Areas of future research may include:
– Review of the literature on AD and/or ADC of defects found
from in-line optical inspections.
– Spatio-temporal analysis [38]
– Comparative studies
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
References
[37]
[38]
[39]
[40]
N. Cressie, Statistics for Spatial Data. New York: Wiley,
1993.
N. Cressie, and C. K. Wikle, Statistics for Spatio-Temporal
Data. New York: Wiley, 2011.
M. Kulldorff, “Tests of spatial randomness adjusted for an
inhomogeneity: A general framework,” Journal of the
American Statistical Association, vol. 101, no. 475, pp.
1289-1305, 2006.
P. A. P. Moran, “The interpretation of statistical maps,”
Journal of the Royal Statistical Society. Series B
(Methodological), vol. 10, no. 2, pp. 243-251, 1948.
Design for Quality Lab
©2012 – CASE’12 – Seoul, Korea – Aug 20_24
Thank You
Q&A