Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Morsier, Frank de; DeMaris, David; Gabrani, Maria; Casati, Nathalie

doi:10.1117/12.2045901

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Pattern Classification1

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2015

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Cited by 2 publications

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“…Image-based classification is another popular technique, 9,15 in which each pattern is rasterized, and then the resulting images are compared pixel-by-pixel, as shown in Fig. 5(c).…”

Section: Pattern Classificationmentioning

confidence: 99%

Topology-oriented pattern extraction and classification for synthesizing lithography test patterns

Shim

Shin

2015

J. Micro/Nanolith. MEMS MOEMS

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A small but diverse set of test patterns is essential for the optimization of lithography parameters. We selectively extract the complicated patterns that are likely to cause lithography defects from test layouts. These patterns are hierarchically classified into groups based on geometric similarity; then, a small number of patterns are chosen to represent each group. We demonstrate this approach in the synthesis of test patterns for metal layers. The total area of the resulting test patterns is only 10% of that of a set produced using a more conventional technique; the resulting hotspot library has 30% fewer patterns, and the time required to create it is cut by an order of magnitude. Shim and Shin: Topology-oriented pattern extraction and classification for synthesizing lithography test patterns Downloaded From: http://nanolithography.spiedigitallibrary.org/ on 05/14/2015 Terms of Use: http://spiedl.org/terms

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“…Image-based classification is another popular technique, 9,15 in which each pattern is rasterized, and then the resulting images are compared pixel-by-pixel, as shown in Fig. 5(c).…”

Section: Pattern Classificationmentioning

confidence: 99%