Given that traditional semi-supervised lithography hotspot detection methods are gradually unable to meet the precision requirements for integrated circuit manufacturing and has difficulty addressing accuracy loss caused by dataset imbalance a new semi-supervised lithography hotspot detection model GSSL is proposed. Methods In this model the Ghost_CBAM was designed by introducing the convolutional block attention module CBAM into the linear change of the Ghost module. The GhostNeck module was designed by combining the Ghost _CBAM module with the squeeze-and-excitation SE network which enabled dimensionality reduction followed by dimensionality expansion of feature maps and established correlations between channels. Then the whole lithography hotspot detection model GSSL was constructed by GhostNeck to realize the semi-supervised learning method of gradually introducing unlabeled data into the training. Data augmentation methods were integrated to augment hotspot layouts in the dataset to alleviate dataset imbalance. Besides a weighted cross-entropy loss function was applied to further improve the model?? s focus on hotspot categories. Results The model was evaluated on the benchmark dataset of the 2012 International Conference on Computer-Aided Design ICCAD competition achieving an average accuracy of 91. 73% and an average false alarm of 680 when the proportion of labeled data ranged from 10% to 50%. Conclusion Compared with other traditional methods GSSL adeptly tackles the challenge of imbalanced datasets improving accuracy in lithography hotspot detection while significantly reducing false alarms. Therefore this study holds considerable application value in lithography hotspot detection