A rule-based computing approach for the segmentation of semiconductor defects

This paper presents a rule-based approach to detect defect patterns and to classify the defect patterns that appear on the semiconductor wafer surfaces. To obtain a general and modular defect pattern detection technique, the proposed approach adopts a hierarchical perspective. A formal analogy has been drawn between the structure of defect patterns and the symptom of disease in clinical practice. The defect patterns to be recognized are viewed as decision made to a particular disease. Design goals include detection of flaws and correlation of defect features based on co-occurrence matrix. The system is capable of identifying the defects on the wafers after die sawing. Each unique defect structure is defined as an object. Objects are grouped into user-defined categories such as chipping, metallization peel off, silicon dust contamination, etc. after die sawing and micro-crack, scratch, ink dot being washed off, bridging, etc. from the wafer.

[1]  Yukio Kosugi,et al.  Automatic Defect Classificatin in Visuall Inspection of Semiconductors Using Neural Networks , 1998 .

[2]  Steven Guan,et al.  A golden-template self-generating method for patterned wafer inspection , 2000, Machine Vision and Applications.

[3]  Rama Chellappa,et al.  A model-based approach for filtering and edge detection in noisy images , 1990 .

[4]  Yoshio Katakura,et al.  Particle measurements in vacuum tools by in situ particle monitor , 1999 .

[5]  Yeng Peng,et al.  Layer yield estimation based on critical area and electrical defect monitor data , 1999, 1999 IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (Cat No.99CH36314).

[6]  Z.W. Zhong,et al.  Rule-Based Inspection of Wafer Surface , 2003, 2003 4th International Conference on Control and Automation Proceedings.

[7]  Bryan Kok Ann Ngoi,et al.  Defect detection in unpolished Si wafers by digital shearography , 2004 .

[8]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[9]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Zhaowei Zhong,et al.  ANALYSIS AND EXPERIMENTS OF BALL DEFORMATION FOR ULTRA-FINE-PITCH WIRE BONDING , 2000 .

[11]  Zhaowei Zhong,et al.  FLIP CHIP ON FR-4, CERAMICS AND FLEX , 2000 .

[12]  Martin A. Hunt,et al.  Automated image registration in semiconductor industry: a case study in the direct-to-digital holography inspection system , 2003, IS&T/SPIE Electronic Imaging.

[13]  Kenneth W. Tobin,et al.  Fuzzy logic connectivity in semiconductor defect clustering , 1998, Electronic Imaging.