卷积神经网络
计算机科学
人工智能
人工神经网络
模式识别(心理学)
深度学习
材料科学
比例(比率)
探测器
计算机视觉
遥感
特征提取
作者
Yu-Chieh Ting,Daw-Tung Lin,Chih-Feng Chen,Bor-Chen Tsai
标识
DOI:10.1007/978-3-030-40605-9_31
摘要
Surface defect inspection is a crucial step during the production process of IC probe. The traditional way of identifying defective IC probes mostly relies on the human visual examination through the microscope screen. However, this approach will be affected by some subjective factors or misjudgments of inspectors, and the accuracy and efficiency are not sufficiently stable. Therefore, we propose an automatic optical inspection system by incorporating the ResNet-101 deep learning architecture into the faster region-based convolutional neural network (Faster R-CNN) to detect the stripping-gold defect on the IC probe surface. The training samples were collected through our designed multi-function investigation platform IMSLAB. To circumvent the challenge of insufficient images in our datasets, we introduce data augmentation using cycle generative adversarial networks (CycleGAN). The proposed system was evaluated using 133 probes. The experimental results revealed our method performed high accuracy in stripping defect detection. The overall mean average precision (mAP) was 0.732, and the defect IC probe classification accuracy rate was 97.74%.
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