CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
<|reference_start|>CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning: 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.<|reference_end|>
@article{liang2024camo:,
    title={CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement
  Learning},
    author={Xiaoxiao Liang, Haoyu Yang, Kang Liu, Bei Yu, Yuzhe Ma},
    journal={arXiv preprint arXiv:2404.00980},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00980},
    primaryClass={cs.CV cs.AR}
}