Optimization of EUV patterning using computational lithography for large-area stochastic hot spot prediction

As semiconductor device scaling continues, the critical dimension CD has continued to decrease. The required CD for advanced device nodes is now beyond the resolution limit of deep ultraviolet (DUV) lithography and extreme ultraviolet lithography (EUVL) is now widely used to meet resolution requirements. Despite continuous improvements in the performance of photoresists, masks, and post-lithography processes, stochastic defects, or stochastic failures 5 - space bridges and line breaks - are still a major factor of yield loss in production14 . Detecting, characterizing, and repairing stochastic defectivity using experimental methods alone is expensive, requiring a lot of wafers, metrology resources and time 9, 15 . We discuss a cost-effective predictive failure probability model - a virtual inspector - and demonstrate how it is an essential tool for the study and reduction of stochastic defectivity in EUVL. We use rigorous probabilistic lithography modeling to construct accurate failure probability models FPMs for EUVL. The computing rate of the FPM admits virtual inspection of full layouts. We confirm that the defectivity predictions of the FPM align well with large-area e-beam inspection results. We demonstrate that the FPM can be used to find the optimal patterning condition and the optical proximity correction OPC that minimize stochastic failures in an EUVL process.