Detection Procedures/Processes of Spatial Defect Patterns (Bins) in Wafers
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Table 2415a. Detection procedures/processes of spatial defect patterns (bins) in wafers. Local and global defects: page4287.

  Characteristics Step 1 Step 2 Step 3 Step 4 Reference
By Yuan et al. A more flexible and less time-consuming approach for analyzing defect data; Each step provides information with different levels of accuracy Defect denoising (Python) Defect clustering Pattern identification Fine tuning [1]

Is based largely on the nearest-
neighbor noise removal technique. [2] It provides information about whether a wafer map contains any clustered local defects. This step separates the global defects from the local defects if local defects exist.

The separated local defects are clustered together for pattern identification. This defect clustering applies the similarity-based robust clustering technique. [3] The purpose of defect clustering is to group the local
defects into several clusters.

The spatial patterns of the defect clusters are identified by using parametric models of the clustered defects. To identify the pattern of each local defect cluster, pattern identification is considered to be a model-selection problem. Different patterns can be described using different models and determined the best model for each local defect cluster.

For more accurate
clustering results, e.g., in terms of yield estimation,
fine tuning can be applied at the final step, which is based on model-based clustering with a new mixture distribution of three different densities: MVNs, PCs, and fuzzy
spherical-shells (SSs). To reduce the computational time required for the fine tuning step, the number of defect clusters and the pattern of each cluster are known a priori in the mixture distribution
Applicable techniques Spatial filter, [4] nearest-neighbor (NN) clutter removal method, [5] support vector clustering [6] Similarity-based clustering method (SCM) [3] Three different models, a multivariate normal MVN, a PC, and a SS had been used to describe the distribution of local defects in a cluster with an amorphous/ linear pattern, a curvilinear pattern, and a closed-ring shaped pattern, respec-
tively. [IAMpage4287] 		 





























 

 

 

 

 

 

 


[1] Tao Yuan, Way Kuo, and Suk Joo Bae, Detection of Spatial Defect Patterns Generated in Semiconductor Fabrication Processes, IEEE Transactions on Semiconductor Manufacturing, 24(3), (2011), 392.
[2] S. Byers and A. E. Raftery, “Nearest-neighbor clutter removal for estimating features in spatial point processes,” J. Am. Stat. Assoc., vol. 93, no. 442, pp. 577–584, 1998.
[3] M.-S. Yang and K.-L. Wu, “A similarity-based robust clustering method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 4, pp. 434–448, Apr. 2004.
[4] C. H. Wang, W. Kuo, and H. Bensmial, “Detection and classification of defects patterns on semiconductor wafers,” IIE Trans., vol. 39, no. 12, pp. 1059–1069, 2006.
[5] S. Byers and A. E. Raftery, “Nearest-neighbor clutter removal for estimating features in spatial point processes,” J. Am. Stat. Assoc., vol. 93, no. 442, pp. 577–584, 1998.
[6] T. Yuan, S. J. Bae, and J. I. Park, “Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering,” Int. J. Adv. Manuf. Technol., vol. 51, pp. 671–683, Dec. 2010.
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