Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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A Study on Classification of Micro-Cracks in Silicon Wafer Through the Fusion of Principal Component Analysis and Neural Network

주성분분석과 신경회로망의 융합을 통한 실리콘 웨이퍼의 마이크로 크랙 분류에 관한 연구

  • Seo, Hyoung Jun (Aeronautical & Mechanical Design Engineering, Graduate School, Korea National University of Transportation) ;
  • Kim, Gyung Bum (Aeronautical & Mechanical Design Engineering, Korea National University of Transportation)
  • 서형준 (한국교통대학교 대학원 항공.기계설계학과) ;
  • 김경범 (한국교통대학교 항공.기계설계학과)
  • Received : 2015.01.07
  • Accepted : 2015.04.07
  • Published : 2015.05.01
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Abstract

Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

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References

  1. Abdelhamid, M., Singh, R., and Omar, M., "Review of Microcrack Detection Techniques for Silicon Solar Cells," IEEE Journal of Photovoltaics, Vol. 4, No. 1, pp. 514-524, 2014. https://doi.org/10.1109/JPHOTOV.2013.2285622
  2. Chiou, Y.-C., Liu, J.-Z., and Liang, Y.-T., "Micro Crack Detection of Multi-Crystalline Silicon Solar Wafer using Machine Vision Techniques," Sensor Review, Vol. 31, No. 2, pp. 154-165, 2011. https://doi.org/10.1108/02602281111110013
  3. Ko, S.-S., Liu, C.-S., and Lin, Y.-C., "Optical Inspection System with Tunable Exposure Unit for Micro-Crack Detection in Solar Wafers," Optik- International Journal for Light and Electron Optics, Vol. 124, No. 19, pp. 4030-4035, 2013. https://doi.org/10.1016/j.ijleo.2012.12.024
  4. Yeon, J. and Kim, G., "Investigation of Laser Scattering Pattern and Defect Detection based on Rayleigh Criterion for Crystalline Silicon Wafer Used in Solar Cell," J. Korean Soc. Precis. Eng., Vol. 28, No. 5, pp. 606-613, 2011.
  5. Byelyayev, A., "Stress Diagnostics and Crack Detection in Full-size Silicon Wafers using Resonance Ultrasonic Vibrations," Ph.D. Thesis, Department of Electrical Engineering, University of South Florida, 2005.
  6. Dallas, W., Polupan, O., and Ostapenko, S., "Resonance Ultrasonic Vibrations for Crack Detection in Photovoltaic Silicon Wafers," Measurement Science and Technology, Vol. 18, No. 3, pp. 852-858, 2007. https://doi.org/10.1088/0957-0233/18/3/038
  7. Moon, H. and Phillips, P. J., "Computational and Performance Aspects of PCA-Based Face- Recognition Algorithms," Perception-London, Vol. 30, No. 3, pp. 303-322, 2001. https://doi.org/10.1068/p2896
  8. Seo, H. J. and Kim, G. B., "Optimal Parameter Selection of Anisotropic Diffusion Filter based on Design of Experiment for Silicon Wafer Crack Detection," J. Korean Soc. Precis. Eng., pp. 905-906, 2014.
  9. Tsai, D.-M., Chang, C.-C., and Chao, S.-M., "Micro- Crack Inspection in Heterogeneously Textured Solar Wafers using Anisotropic Diffusion," Image and Vision Computing, Vol. 28, No. 3, pp. 491-501, 2010. https://doi.org/10.1016/j.imavis.2009.08.001
  10. Lee, D., Hong, S., Cho, S., and Joo, W., "A Study on the Pattern Recognition of Hole Defect using Neural Networks," J. Korean Soc. Precis. Eng., Vol. 20, No. 2, pp. 146-153, 2003.
  11. Pal, S. K. and Mitra, S., "Multilayer Perceptron, Fuzzy Sets, and Classification," IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 683-697, 1992. https://doi.org/10.1109/72.159058

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