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.

在本文中，我们提出了一种AI（人工智能）解决方案，用于焊球HIP（枕中头）缺陷检查。HIP缺陷会影响焊球的导电性，从而导致间歇性故障。由于HIP缺陷的位置和形状变化，传统的机器视觉算法无法完全解决问题。近年来，卷积神经网络（CNN）在图像识别和分类方面具有出色的性能，但是由于数据不足，很容易引起过度拟合的问题。因此，我们将CNN与机器学习算法支持向量机（SVM）结合起来设计检查过程。提及几种最新模型的优势，我们提出了3D CNN模型，并采用了焦点损失和三重态损失来解决由稀有缺陷数据引起的数据不平衡问题。与几种经典的CNN模型和深度学习检测软件SuaKIT相比，我们的检测方法具有最佳的性能和更快的测试速度。