CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917627321909248 |
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| author | Liang, Xiaoxiao Yang, Haoyu Liu, Kang Yu, Bei Ma, Yuzhe |
| author_facet | Liang, Xiaoxiao Yang, Haoyu Liu, Kang Yu, Bei Ma, Yuzhe |
| contents | Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the movements of neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on both via layer patterns and metal layer patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning Liang, Xiaoxiao Yang, Haoyu Liu, Kang Yu, Bei Ma, Yuzhe Computer Vision and Pattern Recognition Hardware Architecture Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the movements of neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on both via layer patterns and metal layer patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry. |
| title | CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning |
| topic | Computer Vision and Pattern Recognition Hardware Architecture |
| url | https://arxiv.org/abs/2404.00980 |