iming at the problem of serious deviation between lithography pattern and mask pattern caused by the mismatch between lithography system and feature size an end-to-end mask optimization framework TransU-ILT based on deep learning was proposed. Methods The framework used the CNN-Transformer hybrid model as a feature extraction module to extract the depth features of the target layout and added a pixel reorganization layer to the feature reconstruction module to reconstruct the mask. In addition in the training process the depth supervision mechanism was added to improve the extraction accuracy of layout pattern features so as to further improve the printability of the mask. Results Quantitative experimental results showed that compared with the most advanced methods the proposed framework achieved a 4X turnaround time acceleration reduced the square L2 error and the process change band index by 13. 4% and 4. 3% respectively and the wafer pattern edge of the mask generated by the framework was smoother and closer to the target layout. Conclusion TransU-ILT is superior to other advanced methods in terms of time performance and mask printability which can provide an effective solution for mask optimization methods