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arXiv · 2404.00980

CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning

Xiaoxiao Liang·Haoyu Yang·Kang Liu·Bei Yu·Yuzhe Ma
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Abstract

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.

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Xiaoxiao Liang, Haoyu Yang, Kang Liu, Bei Yu, Yuzhe Ma. 2024-04-01. CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning. https://arxiv.org/abs/2404.00980

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