For the problems of low recognition rate and slow recognition speed of traditional detection methods in IC appearance defect detection, we propose an IC appearance defect detection algo-rithm IH-ViT. Our proposed model takes advantage of the respective strengths of CNN and ViT to acquire image features from both local and global aspects, and finally fuses the two features for decision making to determine the class of defects, thus obtaining better accuracy of IC defect recognition. To address the problem that IC appearance defects are mainly reflected in the dif-ferences in details, which are difficult to identify by traditional algorithms, we improved the tra-ditional ViT by performing an additional convolution operation inside the batch. For the problem of information imbalance of samples due to diverse sources of data sets, we adopt a dual-channel image segmentation technique to further improve the accuracy of IC appearance defects. Finally, after testing, our proposed hybrid IH-ViT model achieved 72.51% accuracy, which is 2.8% and 6.06% higher than ResNet50 and ViT models alone. The proposed algorithm can quickly and accurately detect the defect status of IC appearance and effectively improve the productivity of IC packaging and testing companies.

翻译：针对传统检测方法在集成电路外观缺陷检测中识别率低、识别速度慢的问题，我们提出了一种集成电路外观缺陷检测算法IH-ViT。所提模型充分利用卷积神经网络（CNN）与视觉Transformer（ViT）各自的优势，从局部和全局两方面获取图像特征，最终融合两类特征进行决策判定缺陷类别，从而获得更优的集成电路缺陷识别精度。针对集成电路外观缺陷主要体现在细节差异上、传统算法难以识别的问题，我们通过批内额外卷积操作改进了传统ViT。针对数据集来源多样导致样本信息不平衡的问题，我们采用双通道图像分割技术进一步提升集成电路外观缺陷检测精度。经测试，提出的混合IH-ViT模型实现了72.51%的准确率，分别比单独使用ResNet50和ViT模型高出2.8%和6.06%。该算法能快速准确检测集成电路外观缺陷状态，有效提升集成电路封装测试企业的生产效率。