Resolution enhancement techniques compatible with an ArF (193 nm) immersion optical lithography system may constitute an effective means of minimizing the size of technology nodes of the dynamic random access memory. This paper investigated one such technique, namely mask optimization (MO), and applied sub-resolution assist features (SRAFs) in the MO to improve the aerial image quality of a target pattern that had undergone optical proximity correction (OPC). This paper first developed an optical model based on the Hopkins model to create an interference map, which was then used to create a cutlevel map. The cut-level map was instrumental in predicting potential SRAF sites and randomly generating SRAFs that would serve as the initial population for a genetic algorithm (GA). Chromosomes were defined as a section map and encoded genes were used to define SRAF and target pattern. Using a GA to identify SRAF geometric measurements and placement was revealed to increase the process window and improve the image performance of the target pattern. This paper used 1D and 2D line/space (L/S) images as the baseline to test the convergence of the proposed method. 2D images were also used to test improvements in aerial image performance. The results indicated that the 1D L/S pattern converged at the 100th iteration. Furthermore, the depths of the focus of the 2D L/S array and 2D contact hole patterns were successfully increased by 113 and 21 nm, respectively. The proposed SRAF method, which integrated the GA and interference map, was able to ensure the diversity of potential SRAF solutions. Moreover, it was able to restrict the SRAF solutions to rectangular structures through the application of mask rules, thereby reducing the cost and improving the feasibility of photomasks. INDEX TERMS Sub-resolution assist feature (SRAF), interference map, genetic algorithm (GA), mask optimization, depth of focus (DOF).
Sequence conservation related to protein function has been discovered via protein sequence alignment and pattern mining. In contrast, our motivation is to mine structure conservation via frequent itemset mining from the viewpoint of structure. In order to describe local structure, neighborhood residue sphere (NRS) is proposed, which is a sphere with 10 A radius of each residue with the combination of sequence and spatial information. Currently, we obtain 56,164 NRSs among 456 EC labels of local conserved region out of total 646 EC labels. In EC label prediction, our experimental results reveal 80.61% Confidence and 53% Accuracy while selecting 1,000 proteins with sequence identity less than 60% from 13,373 enzymes among 563 EC labels. Due to the coverage rate is around 80% higher than CSA and Protemot, the Confidence is almost doubled in comparing with CSA and Protemot. In this study, we choose alternative to figure out function-related local structure without using protein binding site information of protein-ligand complexes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.