A Fast Process-Variation-Aware Mask Optimization Algorithm With a Novel Intensity Modeling

摘要

With the continuous shrinkage of advanced technology nodes into the sub-16-nm regime, optical proximity correction (OPC) is still the main stream to preserve acceptable wafer image quality under lithographic process variations in the foreseeable future. However, OPC is getting more aggressive to keep pace with advanced technology nodes. This results in complex mask solutions and long computation time. In this paper, we propose a novel-intensity-based OPC algorithm to find mask solutions with minimal edge placement error and process variability band area within a short computation time. This is achieved through exploiting a fast novel intensity estimation model with acceptable estimation accuracy to guide the OPC response including two-fragment shifting, corner hammering, and subresolution assist feature insertion for better convergence. Moreover, our algorithm is extended to satisfy the mask notch rule and reduce shot count for a lower mask manufacturing cost. The experimental results show that our algorithm outperforms recently published algorithms on the public benchmarks.