No abstract

Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissection method is presented. Based on the recognition of yield related mask context, the dissection result provides sufficient degrees of freedom to keep fidelity on critical sites while still retaining the frugality of modified designs. Experiments show that the final mask volume using the new method is reduced to about 50% of the conventional method.