  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.
