3D CT Slice Image-Based Algorithm for Non-Wet Defect Inspection in Solder Joints

This paper presents a robust inspection framework for detecting non-wet defects in semiconductor solder joints using 3D CT slice imaging and supervised learning. The proposed method leverages a slice-level ResNet18 classifier combined with a tunable classification confidence parameter to predict defective slices. These slice-level predictions are then aggregated to determine the volume-level defect status through a slice-counting strategy. To accommodate varying defect characteristics across semiconductor packages, we introduce an adjustable defect count threshold and validate its impact on detection performance. Experiments show that the method achieves perfect recall with zero false positives under optimal settings and maintains a stable range across thresholds, outperforming traditional unsupervised and feature-based baselines. The proposed approach is lightweight, adaptable, and requires no retraining to adjust sensitivity, making it well-suited for deployment in real-world inspection pipelines. This work demonstrates the practical synergy of 3D imaging and machine learning in enhancing reliability and efficiency in semiconductor manufacturing. Our codes and data are released at here.