Presentation
CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
DescriptionOptical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches have been proposed, which are typically data-driven and hardly involve particular considerations of the OPC problem, leading to potential performance bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on via patterns and metal patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry.
Event Type
Research Manuscript
TimeWednesday, June 262:15pm - 2:30pm PDT
Location3004, 3rd Floor
Design
Design for Manufacturability and Reliability