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.

翻译：光学邻近校正（OPC）是现代超大规模集成电路制造中确保可打印性的关键步骤。基于机器学习的各类OPC方法已被提出以追求性能和效率，这些方法通常为数据驱动型，极少涉及OPC问题的特定考量，导致潜在的性能或效率瓶颈。本文提出CAMO——一种基于强化学习的OPC系统，该系统深度融合了OPC问题的重要原理。CAMO明确引入了相邻线段移动的空间相关性，以及一种受OPC启发的动作选择调制机制。实验在通孔层图案和金属层图案上进行，结果表明CAMO在学术和工业领域的现有最优OPC引擎中均表现更优。