TsanKit: artificial intelligence for solder ball head-in-pillow defect inspection

In this paper, we propose an AI (Artificial Intelligence) solution for solder ball HIP (Head-In-Pillow) defect inspection. The HIP defect will affect the conductivity of the solder balls leading to intermittent failures. Due to the variable location and shape of the HIP defect, traditional machine vision algorithms cannot solve the problem completely. In recent years, Convolutional Neural Network (CNN) has an outstanding performance in image recognition and classification, but it is easy to cause overfitting problems due to insufficient data. Therefore, we combine CNN and the machine learning algorithm Support Vector Machine (SVM) to design our inspection process. Referring to the advantages of several state-of-the-art models, we propose our 3D CNN model and adopt focal loss as well as triplet loss to solve the data imbalance problem caused by rare defective data. Our inspection method has the best performance and fast testing speed compared with several classic CNN models and the deep learning inspection software SuaKIT.

Automated summary

This paper proposes an AI solution for solder ball HIP defect inspection, combining CNN and SVM algorithms, with a 3D CNN model and focal loss as well as triplet loss to address data imbalance issues caused by rare defective data. The inspection method outperforms classic CNN models and the deep learning inspection software SuaKIT in terms of performance and testing speed.