The comparison of OPC performance and run time for dense versus sparse solutions

Abstract

The lithographic processes and resolution enhancement techniques (RET) needed to achieve pattern fidelity are becoming more complicated as the required critical dimensions (CDs) shrink. For technology nodes with smaller devices and tolerances, more complex models and proximity corrections are needed and these significantly increase the computational requirements. New simulation techniques are required to address these computational challenges. The new simulation technique we focus on in this work is dense optical proximity correction (OPC). Sparse OPC tools typically require a laborious, manual and time consuming OPC optimization approach. In contrast, dense OPC uses pixel-based simulation that does not need as much manual setup. Dense OPC was introduced because sparse simulation methodology causes run times to explode as the pattern density increases, since the number of simulation sites in a given optical radius increases. In this work, we completed a comparison of the OPC modeling performance and run time for the dense and the sparse solutions. The analysis found the computational run time to be highly design dependant. The result should lead to the improvement of the quality and performance of the OPC solution and shed light on the pros and cons of using dense versus sparse solution. This will help OPC engineers to decide which solution to apply to their particular situation.