Abstract-Double/Multiple-patterning (DP/MP) lithography in a multiple litho-etch steps process is a favorable solution for technology scaling to the 20nm node and below. Mask-assignment conflicts represent the biggest challenge for MP and limiting them through design rules is crucial for the adoption of MP technology. In this paper, we offer a methodology for the early evaluation and exploration of layout and MP rules intended for speeding up the rules-development cycle. Using a novel wiringestimation method, we create layout estimates with fine-grained congestion prediction. MP-conflicts are then predicted using a machine-learning approach. In this work, we demonstrate the use of the method for double-patterning lithography in litho-etchlitho-etch process; the methodology is more general, however, and can be applied for other multiple-patterning technologies including tripe/multiple-patterning with multiple litho-etch steps, selfaligned double patterning (SADP), and directed self-assembly. Results of testing the methodology on standard-cell layouts show an 81% accuracy in DP-conflicts prediction. The methodology was then used to explore DP and layout rules and investigate their effects on DP-compatibility and layout area. The methodology allows for rules optimization; for example, pushing the minimum tip-to-side same-color spacing rule value from 1.7× to 1.5× the minimum side-to-side spacing design rule (i.e., from 110nm down to 90nm) would more than double the number of DP-compatible cells in the library.
This paper proposes a region based approach using Scale Invariant Feature Transform (SIFT) and fuzzy logic for computing region based image similarity. When SIFT algorithm is used for matching images, a meaningful or semantic match according to human perception is not obtained. Hence to refine its output, a region based approach has been proposed. When a test image is compared with reference image, SIFT descriptors are computed and the images are segmented into regions and labeled. SIFT similarity measure along with the region information is given as input to fuzzy logic to determine region based similarity measure. Experiments are done using real world optical images, Caltech image datasets and a few satellite images. The proposed approach is found to be efficient for optical images, Caltech image datasets and have good performance for satellite images.
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