Abstract
Packaging the integrated circuit (IC) chip is a necessary step in the manufacturing process of IC products. In general, wafers with the same size and process should have a fixed number of packaged dies. However, many factors decrease the number of the actually packaged dies, such as die scratching, die contamination, and die breakage, which are not considered in the existing die-counting methods. Here we propose a robust method that can automatically determine the number of actual packaged dies by using machine vision techniques. During the inspection, the image is taken from the top of the wafer, in which most dies have been removed and packaged. There are five steps in the proposed method: wafer region detection, wafer position calibration, dies region detection, detection of die sawing lines, and die number counting. The abnormal cases of fractional dies in the wafer boundary and dropped dies during the packaging are considered in the proposed method as well. The experimental results show that the precision and recall rates reach 99.83% and 99.84%, respectively, when determining the numbers of actual packaged dies in the 41 test cases.
Highlights
Machine vision has been widely used in various fields because only simple devices are required, and various solutions have been proposed for different kinds of applications, such as industrial measurement [1], text recognition [2], finger recognition [3], medical image analysis [4], face recognition [5], and human computer interfaces [6]
The number of dies per wafer can be determined by existing counting algorithms [11,12,13,14], which can be found on websites [15] and [16] as well
The number of gross dies per wafer can be estimated by entering the parameters such as wafer size, die width, die height, horizontal and vertical spacing
Summary
Machine vision has been widely used in various fields because only simple devices are required, and various solutions have been proposed for different kinds of applications, such as industrial measurement [1], text recognition [2], finger recognition [3], medical image analysis [4], face recognition [5], and human computer interfaces [6]. The number of dies per wafer can be determined by existing counting algorithms [11,12,13,14], which can be found on websites [15] and [16] as well. In these methods, the number of gross dies per wafer can be estimated by entering the parameters such as wafer size, die width, die height, horizontal and vertical spacing. De Vries pointed out that there are different formulas used by different semiconductor manufacturing companies [13] He found that the accuracy of an exact count algorithm depends on the die area and the aspect ratio. All the above methods require the input of the dimensions of the die and wafer
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