Wafer pre-aligner system based on vision information processing

Information Technology Journal

Volumn 6, Issue 8, 2007, Pages 1245-1251

  Ming, Cong ^a   Xiangji, Kong ^a   Yu, Du ^a   Jing, Liu ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Edge identification; Information processing; Light intensity; Pre aligner system; Vision system

Indexed keywords

ALGORITHMS; COMPUTER VISION; REGRESSION ANALYSIS; WAVELET ANALYSIS;

EDGE IDENTIFICATION; LEAST SQUARES REGRESSION FITTING; LIGHT INTENSITY; POSITION IDENTIFICATION; VISION INFORMATION PROCESSING; VISION SYSTEMS; WAFER PRE ALIGNER SYSTEMS; WAFER WELLS; WEIGHTED MEANS;

DATA PROCESSING;

EID: 37249060664     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2007.1245.1251     Document Type: Article

References (20)

1. Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Prairie Agric. Mach. Inst., 8: 679-698.
2. Chelbeyd, H.P.V., 1996. Automatic gradient threshold determination for edge detection. IEEE Trans. Image Process., 5: 784-787.
3. Cong, M., D. Yu, Z. Chuangsi and K. Xiangji et al., 2006. A kind of wafer pre-alignment device. China, Patent No. 200610048031.x.
4. Dellecker, R., 2001. Boundary-scan bursts into the modern production facility. IEEE Aerospace Electron. Syst., 16:21-24.
5. Donoho, D.L., 1995. Denoising by soft thresholding. IEEE Trans. Inform. Theory, 41: 613-627.
6. Feddema, J.T. et al., 1998. CAD-driven microassembly and visual servoing. IEEE Int. Conf. Robotics Automation, Leuven, Belgium.
7. Hansen, M. and B. Yu, 2000. Wavelet thresholding via mdl for natura limages. IEEE Trans. Inform. Theory, 46: 1778-1788.
8. Huang, S.N., K.K. Tan and T.H. Lee, 2002. Adaptive motion control for a class of nonlinear using neural networks. Automatica, 38: 227-233.
9. Inenaga, M., Y. Arinaga and S. Katsuda et al., 2004. Wafer Pr, alignment Apparatus. USA, Patent No. GUS20040056216A1.
10. Kamaled, N., S. Setarehdan and J.S. John, 1999. Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multi-scale edge detection. IEEE Trans. Biomed. Eng., 46: 1364-1367.
11. Kawamura, Y., T. Yamamoto and N. Yokoyama et al., 1998. A wafer-handling interface under processing ambient conditions for a single-wafer cluster tool. IEEE Trans. Semiconductor Manufac., 11: 13-19.
12. Law, T., I.H. Hdenor and H. Seki, 1996. Image filtering, edge detection and edge tracing using fuzzy reasoning. IEEE Trans. Pattern Anal. Mach. Intelli., 18: 481-491.
13. Li, Z., W. Huakun and Y. Feng, 2004. Localization for Robot Mowers covering unmarked operational Area. Proc. IEEE/RSJ Int. Conf. Intelli. Robot Syst., 3: 197-202.
14. Mallat, S.H. and L. Wangw, 1992. Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory, 38: 617-634.
15. Mard, C. et al., 2001. Fuzzy Adoptive edge definition based on the complexity of the image. The 10th IEEE Int. Conf. Fuzzy Syst. Melbourne, Aust., pp: 675-678.
16. Ming, C., Z. Yumin and J. Ying, 2005. An automated wafer-handling system based on the integrated circuit equipments. Proc. IEEE Int. Conf. Robotics and Biomimetics, Hong Kong and Macau, China, pp: 240-245.
17. Neil, C., R. Robert and S. Alexander, 2000. Finding Optimal Path Maps for Planning across Weighted Regions. Int. J. Robotics Res., 19: 83-95.
18. Nelson, B.J., Y. Zhou and B. Vikramaditya, 1998. Sensor-Based Microassembly If Hybrid MEMS Devices. Control Syst. Mag. IEEE, 18: 35-45.
19. Popa, D., B.H. Kang and J. Sin et al., 2002. Reconfigurable microassembly system for photonics applications. IEEE Int. Conf. Robotics Automat., Washington DC.
20. Russoe, F., 1998. Edge detection in noisy images using fuzzy reasoning. IEEE Trans. Instrumentation Measurement, 47: 1102-1105.