# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 3 Ultrasonic Image Data Processing and Feature Extraction
### Feature Extraction of Solder Joint Image  
The construction of the solder joint failure model relies on the mean intensity of the central region of solder joint images as a key degradation indicator. The central region is chosen for feature extraction based on the structural characteristics of flip-chip packaging, as illustrated in Figure 7. During thermal cycling experiments, solder joints experience thermal stress caused by mismatched thermal expansion between materials. This stress is  
Figure 5: Schematic diagram of image calibration for flip chips. **(a)** Perfectly horizontally calibrated flip-chip image; **(b)** uncalibrated flip-chip image. A and C are the top-left and bottom-right corners of the flip chip, respectively. B and D are the projections of A and C on the \(y\)- and \(x\)-axes. Figure 6: Segmentation results of solder joint images at different locations. particularly concentrated at the interfaces between the solder joints and adjacent materials, with the interface between the solder joint and the chip being especially prone to defects. Since the ultrasonic microimaging system collects data from directly above the solder joints, the most defect-prone area corresponds to the region within the black circular ring of the solder joint image. The black circular ring arises from the edge effect, where the spherical geometry of the solder joint scatters portions of the ultrasonic waves, leading to information loss. As a result, the real edges of the damaged regions are obscured within this black circular ring. Therefore, accurate identification of these concealed edges is critical for ensuring precise feature extraction. In previous studies conducted by our group, it was observed that the real edges of the damaged regions hidden within the edge effect were closely associated with the local minima of grayscale values in the solder joint images [21]. By calculating the grayscale minima of each row and column, the real edges of the damaged regions can be accurately determined. This approach effectively addresses the challenges posed by the edge effect and ensures accurate feature extraction. Figure 8 illustrates the evolution of the damage region edges for the same solder joint throughout its complete life cycle obtained using this method. The numerical labels below the solder joint images correspond to the thermal cycling experiment periods. To verify the effectiveness of the method used in this paper, a comparison with other edge detection techniques is presented in Figure 9. After thermal cycling experiments, some solder joints exhibit significant changes in the area, shape, and intensity of the central region due to material flow at elevated temperatures. In Figure 9, the leftmost column contains three solder joint images characterized by edge fractures, high noise, and distortion, respectively. Figure 8: Damage Area edge extraction of the same solder joint at different cycles of thermal cycling test. Figure 7: Schematic diagram of flip-chip package structure. The detection results highlight the limitations of threshold-based edge detection methods, such as the Canny and Otsu algorithms [22; 23]. In solder joint images, the grayscale variation within the dark ring is often subtle, making it challenging to select an appropriate threshold. A threshold that is too high results in edge information loss, leading to discontinuous edges, while a threshold that is too low introduces numerous false edges, compromising the accuracy of subsequent analysis. Gradient-based edge detection methods, such as the Prewitt operator [24], are highly sensitive to noise in the images that may originate from various sources such as equipment vibrations or electronic interference, significantly affecting local pixel intensities and causing the algorithm to erroneously detect nonexistent edges. Although filtering techniques can reduce noise to some extent, they often come at the cost of image detail, ultimately reducing the accuracy of edge detection.