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.
@article{arxiv.2404.00980,
title = {CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning},
author = {Xiaoxiao Liang and Haoyu Yang and Kang Liu and Bei Yu and Yuzhe Ma},
journal= {arXiv preprint arXiv:2404.00980},
year = {2024}
}