Research Papers

Study on the Die-Attach Voids Distribution With X-Ray and Image Processing Techniques

[+] Author and Article Information
Aleksander Sesek

Laboratory of Microelectronics,
Department of Microelectronic Technology,
Faculty of Electrical Engineering,
University of Ljubljana Slovenia,
Ljubljana 1000, Slovenia
e-mail: aleksander.sesek@fe.uni-lj.si

Olga Chambers

Laboratory of Microelectronics,
Department of Microelectronic Technology,
Faculty of Electrical Engineering,
University of Ljubljana Slovenia,
Ljubljana 1000, Slovenia
e-mail: olga.chambers@fe.uni-lj.si

Janez Trontelj

Laboratory of Microelectronics,
Department of Microelectronic Technology,
Faculty of Electrical Engineering,
University of Ljubljana Slovenia,
Ljubljana 1000, Slovenia
e-mail: janez.trontelj@fe.uni-lj.si

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received May 25, 2018; final manuscript received February 5, 2019; published online March 4, 2019. Assoc. Editor: Toru Ikeda.

J. Electron. Packag 141(2), 021005 (Mar 04, 2019) (7 pages) Paper No: EP-18-1040; doi: 10.1115/1.4042804 History: Received May 25, 2018; Revised February 05, 2019

Power electronic components' reliability depends, to a great extent, on the quality of die-attach technology. The voids appearance in the die-attach regions is almost unavoidable during the manufacturing process. The aim of this paper is to demonstrate that image processing tools enable fast and accurate void segmentation, while reducing manual interaction for X-ray monitoring of imperfect power transistor die soldering. The most common void parameters such as void area, void distribution, and shape roundness were extracted and used for statistical analysis.

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Fig. 3

An example of the region of interest border recognition with proposed registration procedure. Left image shows registered region of the interest in the image. Middle image shows result of the Canny edge-detection method. Right image shows the recognized die-attach borders.

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Fig. 2

Left image: an example of the X-ray image from Dataset 1. Right image: the corresponding reference mask, where circles indicate position of the mounting pins and rectangles correspond position of the die-attach in X-ray image.

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Fig. 1

The regions of the chip from each research dataset. Rectangles indicates die-attach region under chip. Voids in the die-attach region are visible as bright spots.

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Fig. 4

The intermediate steps during voids segmentation with proposed method: (a) registered die-attach region; (b) contrast enhanced image using histogram scale adjustment; (c) light structure suppression; (d) result of the segmentation with adaptive thresholding applied to the region (c); (e) result of the filled closed edges detected with Canny operator applied to the region (a); (f) segmentation results of the voids with proposed method that incorporates results of the (d) and (e)

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Fig. 5

Results of the voids segmentation in the die-attach region using proposed method for research datasets

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Fig. 6

Comparison of voids segmentation results using different image processing techniques: (a) registered region of the interest with proposed methodology; (b) region of the interest with voids manual annotation performed by an expert; (c) result with light structure suppression and adaptive thresholding; (d) result obtained using Canny edge-detection method; (e) result obtained using fuzzy c-mean clustering; and (f) result obtained using proposed method

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Fig. 7

Frequency (bars) and probability density (curves) of void shape circularity extracted from dataset 1 (left column), dataset 2 (middle column), and dataset 3 (right column). Fitted functions: gamma function, lognormal function, normal function, and Weibull density function.

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Fig. 8

Frequency (bars) and probability density (curves) of different void characteristics in the die-attach region extracted from dataset 1 (first row), dataset 2 (second row), and dataset 3 (third row). First column is the probability density for max void area in die-attach region; second column is probability density of number of voids in die-attach region; third and fourth columns are probability density of total voids area and single void area in die-attach, respectively. Fitted functions: gamma function, lognormal function, normal function, and Weibull density function.

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Fig. 9

Probability maps of void center appearance for three different product packages. Left image shows the region formation scheme, where di, i = 1… n is the magnitude of the region erosion, n—required number of regions. Black regions represents probability appearance of the die-attach void, where the highest probability corresponds white point. Plots below show correlation between frequency appearance of the void and region size for corresponding die-attach regions. Continuous lines correspond the fitted exponential functions.



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