CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement
  Learning
Xiaoxiao Liang Haoyu Yang Kang Liu Bei Yu Yuzhe Ma
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.