In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to gui...In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.展开更多
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 dissec- ti...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 dissec- tion 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.展开更多
文摘In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.
文摘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 dissec- tion 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.
