# An Automatic Detection Method for Cutting Path of Chips in Wafer
## 2 Materials and Methods
### Automatic Detection Algorithm of Chip Cutting Path  
The real wafer image has defects such as uneven brightness distribution in the chip region, inconsistent geometric shape, and pollution in the chip region and the street. The image in Figure 4 is defined and classified according to the defects in the chip region. Figure 4a has the highest proportion of images in different categories. Compared with other categories of images, all chip regions in the image have uniform gray distribution and obvious interlayers, which are classified as qualified images. Region A in Figure 4a is a good chip region. Figure 4b is classified as an overexposed image. The image is generally bright, and there is a defect of uneven gray distribution in some chip regions. In Figure 4b, region B differs greatly from the area on the right. The chip contour is poorly differentiated from the street, and the interlayer is not obvious. Figure 4c is classified as too dark, as the image is generally dark and some chip regions appear as black particles, as shown in the region marked C in Figure 4c. Figure 4d is defined as an image with surface contamination. Individual chips are marked by ink dots because they fail to pass the electrical test or the photolithographic etching process is unqualified, resulting in a large area of black spots on the chip surface. When the above multiple chip regions appear in the wafer image, the processing method is the same. In Figure 4, regardless of what category the image is, there is pollution in the street. The shape of the chip region is roughly the same, but there is slight or obvious deformation in the interlayer, such as region D in Figure 4d. In order to adapt to the above four categories of wafer images and locate the cutting path accurately and quickly, the cutting path positioning algorithm is divided into four steps: Wafer alignment, image brightness calibration, chip region positioning, and cutting path determination. The algorithm flow is shown in Figure 5. The coordinate systems in Figure 5 are the space coordinate system _p-ij_ where the wafer is located, the image coordinate system _q-uv_, and the motion coordinate system _o-xy_. To accurately determine the spatial position of the wafer through the micro vision system and make the laser cutter  
Figure 4: Classification of wafer images. (**a**) Wafer images classified as category 1; (**b**) wafer images classified as category 2; (**c**) wafer images classified as category 3; (**d**) wafer images classified as category 4.  
move to the starting position of the cutting path and move along the cutting path, it is necessary to calibrate the coordinate system and adjust the three coordinate systems to be parallel to each other and in the same direction. After the coordinate system is calibrated, we used the method proposed by a previous study [17] to align the rotating wafer. The brightness of the aligned wafer image is calibrated to improve the gray distribution of the image, highlight the chip region, and improve the image quality. Based on the brightness calibration, we selected the chip matching template and the template that limits the size of the area where the interlayer is located (the area-limiting template only serves to determine the area where the chip interlayer is located and does not perform any image processing). Then, the chip region and the center of gravity position (only one template is selected for wafers of the same specification) were determined by the template matching algorithm to calculate the frequency domain correlation, and then the area where the chip interlayer is located was determined by the region limit template to reduce the search range and improve the accuracy.