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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10255 |
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| _version_ | 1866909901747388416 |
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| author | Jin, Qian Liu, Yumeng Jiang, Yuqi Sun, Qi Zhuo, Cheng |
| author_facet | Jin, Qian Liu, Yumeng Jiang, Yuqi Sun, Qi Zhuo, Cheng |
| contents | Reliable, generalizable data foundations are critical for enabling large-scale models in computational lithography. However, essential tasks-mask generation, rule violation detection, and layout optimization-are often handled in isolation, hindered by scarce datasets and limited modeling approaches. To address these challenges, we introduce Unitho, a unified multi-task large vision model built upon the Transformer architecture. Trained on a large-scale industrial lithography simulation dataset with hundreds of thousands of cases, Unitho supports end-to-end mask generation, lithography simulation, and rule violation detection. By enabling agile and high-fidelity lithography simulation, Unitho further facilitates the construction of robust data foundations for intelligent EDA. Experimental results validate its effectiveness and generalizability, with performance substantially surpassing academic baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10255 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unitho: A Unified Multi-Task Framework for Computational Lithography Jin, Qian Liu, Yumeng Jiang, Yuqi Sun, Qi Zhuo, Cheng Machine Learning Reliable, generalizable data foundations are critical for enabling large-scale models in computational lithography. However, essential tasks-mask generation, rule violation detection, and layout optimization-are often handled in isolation, hindered by scarce datasets and limited modeling approaches. To address these challenges, we introduce Unitho, a unified multi-task large vision model built upon the Transformer architecture. Trained on a large-scale industrial lithography simulation dataset with hundreds of thousands of cases, Unitho supports end-to-end mask generation, lithography simulation, and rule violation detection. By enabling agile and high-fidelity lithography simulation, Unitho further facilitates the construction of robust data foundations for intelligent EDA. Experimental results validate its effectiveness and generalizability, with performance substantially surpassing academic baselines. |
| title | Unitho: A Unified Multi-Task Framework for Computational Lithography |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.10255 |