Automated image analysis for evaluation of wafer backside chipping
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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.
KeywordsDicing Silicon wafer Chipping Edge detection Automated inspection
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This work was supported by the Ministry of Education and Science of Russian Federation (project no. 14.580.21.0009, unique identifier RFMEFI58017X0009).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 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, IndiaGoogle 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 CrossRefGoogle Scholar
- 9.Levinson G (2011) Process optimization of dicing microelectronic substratesGoogle Scholar
- 13.Cheung AT Dicing advanced materials for microelectronics. In: Proceedings International Symposium on Advanced Packaging Materials: Processes, Properties and Interfaces, 2005. IEEE, pp 149–152Google Scholar
- 14.Takeda K, Tsushima T (2014) Kerf inspecting method and kerf inspecting system of the dicing deviceGoogle Scholar
- 15.Ma L, Bao SX (2010) Failure analysis of cracked die. In: ECS transactions. The electrochemical Society, pp 281–287Google Scholar
- 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 1–7Google Scholar
- 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 3:310–315Google Scholar
- 18.Gonzalez RC, Woods RE (2008) Digital image processing. Prentice Hall, Upper Saddle RiverGoogle 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 33–43Google 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 1–4Google 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 335–338Google Scholar
- 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 475–479Google Scholar
- 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 40011Google 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 1–5Google 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 72–76Google Scholar
- 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 1–6Google Scholar
- 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 561–564Google Scholar
- 36.Curvature and Radius of Curvature. https://www.math24.net/curvature-radius/. Accessed 7 Aug 2018