Modeling accuracy and TAT improvements for next generation mask error correction

Abstract

For advanced technology nodes, leading edge mask fidelity is ensured by applying a mask error correction (MEC) solution that mitigates the distortions caused by the many proximity effects present while manufacturing these masks. The centerpiece of the MEC solution is a compact model that captures these systematic effects and allows the accurate prediction of the printed signature on the mask for any layout slated for the process in consideration. In addition, time and resources constraints at the fab dictate that the model must be efficient enough to make the actual step of correcting the mask a fast and practical one. The introduction of advanced modeling schemes based on machine learning is providing new dimensions for the exploration of this perennial balance between accuracy and speed, including in the context of lithography mask models. This has been a focus of development at Synopsys as multiple routes have been implemented, with promising outcomes for mask model accuracy and correction turnaround time (TAT) performance. This paper provides examples of the improvements as well as comparisons between the different approaches, showing how deep learning can be leveraged to significantly increase mask model accuracy while keeping simulation and correction TAT within acceptable limits.

Enhancing mask synthesis for curvilinear masks in full-chip extreme ultraviolet lithography

“…As new and existing mask exposure and etching processes are pushed to handle higher resolution and mask pattern complexity, MEC modeling and correction become an increasingly important mask synthesis process step, e.g., Ref. 16. as parametric curves, we can reduce the MEC output data volume by controlling the control point density and curve sampling density for the MEC output mask.…”