An unsupervised neural network approach for automatic semiconductor wafer defect inspection

Expert Systems with Applications

Volumn 36, Issue 1, 2009, Pages 950-958

  Chang, Chuan Yu ^a   Li, ChunHsi ^b   Chang, Jia Wei ^a   Jeng, MuDer ^b  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL TAIWAN OCEAN UNIVERSITY   (Taiwan)

Author keywords

Self organizing neural network; Unsupervised learning; Wafer inspection

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHIP SCALE PACKAGES; COMPUTER VISION; ELECTRIC CONDUCTIVITY; ELECTRONIC EQUIPMENT MANUFACTURE; ELECTRONICS PACKAGING; IMAGE CLASSIFICATION; INSPECTION; NETWORK PROTOCOLS; SCANNING ELECTRON MICROSCOPY; SEMICONDUCTOR MATERIALS; SENSOR NETWORKS; STRONTIUM COMPOUNDS; VEGETATION;

DEFECT INSPECTION; DEFECTIVE REGIONS; DIE PACKAGING; HUMAN FATIGUE; LEARNED PATTERNS; P RIOR WORK; PERSONNEL COSTS; PRODUCT FLEXIBILITY; REAL-WORLD DATA; SCANNING ELECTRON MICROSCOPES; SELF-ORGANIZING NEURAL NETWORK; SELF-ORGANIZING NEURAL NETWORKS; SEMI-CONDUCTOR WAFER; SUPERVISED NEURAL NETWORKS; UNSUPERVISED LEARNING; UNSUPERVISED NEURAL NETWORKS; WAFER INSPECTION;

NEURAL NETWORKS;

EID: 53849083825     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.10.033     Document Type: Article

References (15)

1. Arivazhagan S., and Ganesan L. Texture classification using wavelet transform. Pattern Recognition Letters 24 (2003) 1513-1521
2. Cheriet M., Said J.N., and Suen C.Y. A recursive thresholding technique for image segmentation. IEEE Transactions on Image Processing 7 (1998) 918-921
3. Dhawan A.P. Medical image analysis (2003), Wiley Interscience
4. Gonzalez R.C., and Woods R.E. Digital image processing. 2nd ed. (2002), Prentice-Hall International, Inc
5. Ham F.M., and Kostanic I. Principles of neurocomputing for science and engineering (2001), McGRAW-Hill
6. Haykin S. Neural networks. 2nd ed. A comprehensive foundation (1999), Prentice-Hall International, Inc
7. Huang C.J., Wu C.F., and Wan C.C. Image processing techniques for wafer defect cluster identification. IEEE Design & Test of Computers 19 2 (2002) 44-48
8. Karayiannis N.B., and Pai P.I. Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization. IEEE Transactions on Medical Imaging 18 2 (1999) 172-180
9. Kulkarni A.D. Computer vision and fuzzy-neural systems (2001), Prentice Hall
10. Mital, D. P. & Teoh, E. K (1991). Computer based wafer inspection system. In Proceedings. IECON'91. 1991. Proceeding of international conference on industrial electronics, control and instrumentation (Vol. 3, pp. 2497-2503).
11. Penedo M.G., Carreria M.J., Mosquera A., and Cabello D. Computer-aided diagnosis: A neural-network-based approach to lung nodule detection. IEEE Transactions on Medical Imaging 17 (1998) 872-880
12. Su C.T., Yang T., and Ke C.M. Neural-network approach for semiconductor wafer post-sawing inspection. IEEE Transactions on Semiconductor Manufacturing 15 2 (2002) 260-266
13. Tobin Jr. K.W., Karnowski T.P., and Lakhani F. Integrated applications of inspection data in the semiconductor manufacturing environment. SPIE, Metrology-based Control for Micro-Manufacturing 4275 (2001) 31
14. Wang, P., Krishnan, S. M., Kugean, C., & Tjoa, M. P. (2001). Classification of endoscopic images based on texture and neural network. Engineering in medicine and biology society, 2001. In Proceedings of the 23rd annual international conference of the IEEE (Vol. 4(25-28), pp. 3691-3695).
15. Zang J.M., Lin R.M., and Wang M.J.J. The development of an automatic post-sawing inspection system using computer vision techniques. Computers in Industry 40 (1999) 51-60