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Automated image analysis for evaluation of wafer backside chipping

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Abstract

When a silicon wafer is cut into separate dies, their front and back sides might have chipping resulting in die cracks and yield loss. To prevent defect formation, silicon wafers should undergo optical inspection for evaluation of wafer chipping, its size, and its shape. This work proposes an automated method of image processing that includes die edge detection, die street search, and determination of chipping size and shape. Die edge search was done using an Otsu’s thresholding method. This technique was chosen out as the optimal of the five ones. The choice was based on the segmentation precision evaluation of two types of images: with sharp and blurred edges. Die street search was done using a developed algorithm capable of processing images with angular displacement. Chipping shape and size were calculated through die edge displacement from the die street. Based on the numerical evaluation of chipping size and shape, a chipping danger metric that may be used for detection of defective dies has been proposed.

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References

  1. JEITA (2017) Production forecasts for the global electronics and information technology industries. https://www.jeita.or.jp/english/topics/2017/1219_en.pdf. Accessed 4 Jul 2018

  2. Swaminathan P (2017) Semiconductor materials, devices and fabrication. Wiley, India

    Google Scholar 

  3. Lei W-S, Kumar A, Yalamanchili R (2012) Die singulation technologies for advanced packaging: a critical review. J Vac Sci Technol B, Nanotechnol Microelectron Mater Process Meas Phenom 30:40801. https://doi.org/10.1116/1.3700230

    Article  Google Scholar 

  4. Pei ZJ, Fisher GR, Liu J (2008) Grinding of silicon wafers: a review from historical perspectives. Int J Mach Tools Manuf 48:1297–1307. https://doi.org/10.1016/J.IJMACHTOOLS.2008.05.009

    Article  Google Scholar 

  5. Tang Y, Fuh JYH, Loh HT et al (2008) Laser dicing of silicon wafer. Surf Rev Lett 15:153–159. https://doi.org/10.1142/S0218625X08011147

    Article  Google Scholar 

  6. Lei YY, Jiang DJ, Liu KF, Tang PH (2011) Experiments on dicing monocrystalline silicon wafer using micro abrasive water jet. Adv Mater Res 287–290:2863–2868. https://doi.org/10.4028/www.scientific.net/AMR.287-290.2863

    Article  Google Scholar 

  7. Luo SY, Wang ZW (2008) Studies of chipping mechanisms for dicing silicon wafers. Int J Adv Manuf Technol 35:1206–1218. https://doi.org/10.1007/s00170-006-0800-3

    Article  Google Scholar 

  8. Lin J-W, Cheng M-H (2014) Investigation of chipping and wear of silicon wafer dicing. J Manuf Process 16:373–378. https://doi.org/10.1016/J.JMAPRO.2014.04.002

    Article  Google Scholar 

  9. Levinson G (2011) Process optimization of dicing microelectronic substrates

  10. Tsai M-Y, Lin CS (2007) Testing and evaluation of silicon die strength. IEEE Trans Electron Packag Manuf 30:106–114. https://doi.org/10.1109/TEPM.2007.899072

    Article  Google Scholar 

  11. Steiert M, Wilde J (2014) Influence of dicing damages on the thermo-mechanical reliability of bare-chip assemblies. Microelectron Reliab 54:1686–1691. https://doi.org/10.1016/J.MICROREL.2014.08.005

    Article  Google Scholar 

  12. Jacob P, Rothkirch W (2008) Unusual defects, generated by wafer sawing: diagnosis, mechanisms and how to distinguish from related failures. Microelectron Reliab 48:1253–1257. https://doi.org/10.1016/J.MICROREL.2008.06.040

    Article  Google Scholar 

  13. Cheung AT Dicing advanced materials for microelectronics. In: Proceedings International Symposium on Advanced Packaging Materials: Processes, Properties and Interfaces, 2005. IEEE, pp 149–152

  14. Takeda K, Tsushima T (2014) Kerf inspecting method and kerf inspecting system of the dicing device

  15. Ma L, Bao SX (2010) Failure analysis of cracked die. In: ECS transactions. The electrochemical Society, pp 281–287

  16. Teo M, Kheng SC, Lee C (2006) Process and material characterization of die attach film (DAF) for thin die applications. In: 2006 international conference on electronic materials and packaging. IEEE, pp 1–7

  17. Basavaprasad B, Ravi M (2014) A comparative study on classification of image segmentation methods with a focus on graph based techniques. Int J Res Eng Technol 3:310–315

    Google Scholar 

  18. Gonzalez RC, Woods RE (2008) Digital image processing. Prentice Hall, Upper Saddle River

    Google Scholar 

  19. López-Leyva R, Rojas-Domínguez A, Flores-Mendoza JP et al (2016) Comparing threshold-selection methods for image segmentation: application to defect detection in automated visual inspection systems. Springer, Cham, pp 33–43

    Google Scholar 

  20. Chaple GN, Daruwala RD, Gofane MS (2015) Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA. In: 2015 International Conference on Technologies for Sustainable Development (ICTSD). IEEE, pp 1–4

  21. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI-8:679–698. https://doi.org/10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

  22. Zhang L (2014) Image adaptive edge detection based on canny operator and multiwavelet denoising. In: International Conference on Computer Science and Electronic Technology. Shenzhen, pp 335–338

  23. Farahanirad H, Shanbehzadeh J, Pedram MM, Sarrafzadeh A (2011) A hybrid edge detection algorithm for salt- and-pepper noise. In: International MultiConference of Engineers and Computer Scientists. Hong Kong, pp 475–479

  24. Feng Y, Zhang J, Wang S (2017) A new edge detection algorithm based on Canny idea. In: AIP Conference Proceedings. AIP Publishing LLC , p 40011

  25. Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc London Ser B, Biol Sci 207:187–217. https://doi.org/10.1098/RSPB.1980.0020

    Article  Google Scholar 

  26. Assirati L, Silva NR, Berton L, Lopes AA, Bruno OM (2014) Performing edge detection by difference of Gaussians using q-Gaussian kernels. J Phys Conf Ser 490:12020. https://doi.org/10.1088/1742-6596/490/1/012020

    Article  Google Scholar 

  27. Clark JJ (1989) Authenticating edges produced by zero-crossing algorithms. IEEE Trans Pattern Anal Mach Intell 11:43–57. https://doi.org/10.1109/34.23112

    Article  MATH  Google Scholar 

  28. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647. https://doi.org/10.1109/34.295913

    Article  Google Scholar 

  29. Chen C-W, Chen M-F, Chen C-Y, et al (2016) An automatic optical system for micro-defects inspection on 5 surfaces of a chip. In: 2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS). IEEE, pp 1–5

  30. Wang C-N, Wang C-N, Chen W-Y (2009) Three-step approach for wafer sawing lane inspection. Opt Eng 48:117204. https://doi.org/10.1117/1.3265714

    Article  Google Scholar 

  31. Xue Mei, Patel NS, Bicen B, Khalsa S (2012) Automated optical inspection for die prep. In: 2012 SEMI Advanced Semiconductor Manufacturing Conference. IEEE, pp 72–76

  32. Lin C-F, Fang H-R, Sze J-R, et al (2016) Real-time image data acquisition and inspection system for integrated circuit wafer after sawing process. In: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings. IEEE, pp 1–6

  33. Pan N, Liu R, Wang M (2010) Locating the centre line of paddle vats for cutting wafer images by using binary segmentation. In: 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science. IEEE, pp 561–564

  34. Lin S-F, Chen C-H, Lo C-Y (2015) Near-infrared imaging system for nondestructive inspection of micro-crack in wafer through dicing tape. Appl Opt 54:E123–E128

    Article  Google Scholar 

  35. Lazzarin P, Filippi S (2006) A generalized stress intensity factor to be applied to rounded V-shaped notches. Int J Solids Struct 43:2461–2478. https://doi.org/10.1016/J.IJSOLSTR.2005.03.007

    Article  MATH  Google Scholar 

  36. Curvature and Radius of Curvature. https://www.math24.net/curvature-radius/. Accessed 7 Aug 2018

  37. Berto F, Lazzarin P (2014) Recent developments in brittle and quasi-brittle failure assessment of engineering materials by means of local approaches. Mater Sci Eng R Reports 75:1–48. https://doi.org/10.1016/J.MSER.2013.11.001

    Article  Google Scholar 

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Funding

This work was supported by the Ministry of Education and Science of Russian Federation (project no. 14.580.21.0009, unique identifier RFMEFI58017X0009).

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Authors and Affiliations

  1. Petrozavodsk State University, 31 Lenina str., Petrozavodsk, 185910, Russia

    Valentin Perminov, Vadim Putrolaynen, Maksim Belyaev, Elena Pasko & Kirill Balashkov

  2. GS Nanotech, 11 Industrialnaya str., Gusev, 238050, Russia

    Elena Pasko & Kirill Balashkov

Authors
  1. Valentin Perminov
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  2. Vadim Putrolaynen
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  3. Maksim Belyaev
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  4. Elena Pasko
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  5. Kirill Balashkov
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Correspondence to Maksim Belyaev.

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Perminov, V., Putrolaynen, V., Belyaev, M. et al. Automated image analysis for evaluation of wafer backside chipping. Int J Adv Manuf Technol 99, 2015–2023 (2018). https://doi.org/10.1007/s00170-018-2647-9

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  • DOI: https://doi.org/10.1007/s00170-018-2647-9

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