CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning

• URL: http://arxiv.org/abs/2404.00980v1
• Date: Mon, 1 Apr 2024 07:52:05 GMT
• Title: CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
• Authors: Xiaoxiao Liang, Haoyu Yang, Kang Liu, Bei Yu, Yuzhe Ma,
• Abstract summary: We propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. Results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry.
• Score: 18.05743927413266
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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