Effective area partitioning for preparing parallel processing in mask data preparation

Yoshiyuki Satou; Yasushi Okamoto; Manabu Fujimoto; Hiroshi Tsuchida; Akiko Satou

doi:10.1117/12.746523

14 November 2007 Effective area partitioning for preparing parallel processing in mask data preparation

Yoshiyuki Satou, Yasushi Okamoto, Manabu Fujimoto, Hiroshi Tsuchida, Akiko Satou

Proceedings Volume 6730, Photomask Technology 2007; 67304C (2007) https://doi.org/10.1117/12.746523
Event: SPIE Photomask Technology, 2007, Monterey, California, United States

Abstract

Mask Data Preparation (MDP), which typically consists of Boolean operations, sizing, mask rule check (MRC) and fracturing, requires intense computing power. For today's increasingly large data, utilizing distributed parallel processing with multiple CPUs or using a host server is an established approach to reduce turn around time (TAT). A data analysis and distributing loads are usually required in its preparation process, however it is inevitably a sequential process by its nature, which severely affects the overall TAT. An inappropriate preparation process causes uneven loads for the parallel processing and leads to an increase of TAT. It is challenging especially when a large number of parallel processing nodes are used. This paper introduces a novel methodology of an efficient parallel processing to run an MDP. The involving tests and analysis have been applied to layout data formats by using MaskStudio version 6 (MS6) fracturing system. MS6 takes the method of grid-based partitioning as the preparing process of parallel processing, and each partition is processed for such as Boolean operation, sizing and MRC. By increasing parallel processing nodes, this methodology successfully showed the reduction of the process time.

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Yoshiyuki Satou, Yasushi Okamoto, Manabu Fujimoto, Hiroshi Tsuchida, and Akiko Satou "Effective area partitioning for preparing parallel processing in mask data preparation", Proc. SPIE 6730, Photomask Technology 2007, 67304C (14 November 2007); https://doi.org/10.1117/12.746523

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KEYWORDS

Parallel processing

Data processing

Parallel computing

Optical proximity correction

Data analysis

Analytical research

Resolution enhancement technologies

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