Presentation
EMOGen: Enhancing Mask Optimization via Pattern Generation
DescriptionLayout pattern generation via deep generative models is a promising methodology for building practical large-scale pattern libraries.
However, although improving optical proximity correction (OPC) is a major target of existing pattern generation methods, they are not explicitly trained for OPC and integrated into OPC methods.
In this paper, we propose EMOGen to enable the co-evolution of layout pattern generation and learning-based OPC methods.
With the novel co-evolution methodology, we achieve up to 39% enhancement in OPC and 34% improvement in pattern legalization.
However, although improving optical proximity correction (OPC) is a major target of existing pattern generation methods, they are not explicitly trained for OPC and integrated into OPC methods.
In this paper, we propose EMOGen to enable the co-evolution of layout pattern generation and learning-based OPC methods.
With the novel co-evolution methodology, we achieve up to 39% enhancement in OPC and 34% improvement in pattern legalization.
Event Type
Research Manuscript
TimeWednesday, June 262:30pm - 2:45pm PDT
Location3004, 3rd Floor
Design
Design for Manufacturability and Reliability