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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2408.08969 |
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| _version_ | 1866909301203795968 |
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| author | Chen, Guojin Yang, Haoyu Ren, Haoxing Yu, Bei Pan, David Z. |
| author_facet | Chen, Guojin Yang, Haoyu Ren, Haoxing Yu, Bei Pan, David Z. |
| contents | Optical proximity correction (OPC) is crucial for pushing the boundaries of semiconductor manufacturing and enabling the continued scaling of integrated circuits. While pixel-based OPC, termed as inverse lithography technology (ILT), has gained research interest due to its flexibility and precision. Its complexity and intricate features can lead to challenges in mask writing, increased defects, and higher costs, hence hindering widespread industrial adoption. In this paper, we propose DiffOPC, a differentiable OPC framework that enjoys the virtue of both edge-based OPC and ILT. By employing a mask rule-aware gradient-based optimization approach, DiffOPC efficiently guides mask edge segment movement during mask optimization, minimizing wafer error by propagating true gradients from the cost function back to the mask edges. Our approach achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques, bridging the gap between the high accuracy of pixel-based OPC and the practicality required for industrial adoption, thus offering a promising solution for advanced semiconductor manufacturing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentiable Edge-based OPC Chen, Guojin Yang, Haoyu Ren, Haoxing Yu, Bei Pan, David Z. Artificial Intelligence Optics Optical proximity correction (OPC) is crucial for pushing the boundaries of semiconductor manufacturing and enabling the continued scaling of integrated circuits. While pixel-based OPC, termed as inverse lithography technology (ILT), has gained research interest due to its flexibility and precision. Its complexity and intricate features can lead to challenges in mask writing, increased defects, and higher costs, hence hindering widespread industrial adoption. In this paper, we propose DiffOPC, a differentiable OPC framework that enjoys the virtue of both edge-based OPC and ILT. By employing a mask rule-aware gradient-based optimization approach, DiffOPC efficiently guides mask edge segment movement during mask optimization, minimizing wafer error by propagating true gradients from the cost function back to the mask edges. Our approach achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques, bridging the gap between the high accuracy of pixel-based OPC and the practicality required for industrial adoption, thus offering a promising solution for advanced semiconductor manufacturing. |
| title | Differentiable Edge-based OPC |
| topic | Artificial Intelligence Optics |
| url | https://arxiv.org/abs/2408.08969 |