CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning

arXiv:2404.00980v1 Announce Type: new
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 …

abstract arxiv correlation cs.ar cs.cv data data-driven efficiency machine machine learning manufacturing modern optical optimization performance reinforcement reinforcement learning type vital