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Evaluation of neighborhood pattern matching algorithm in MPC processing
Author(s): Kohei Yanagisawa; Kota Kobayashi; Kiyoshi Kageyama; Sebastian Munoz; Fabian Saso; Parikshit Kulkarni; John Valadez; Kokoro Kato
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Paper Abstract

Model Based Mask Process Correction (MB-MPC) has been deployed in the photomask manufacturing process for almost a decade. It has now become a must have process for leading edge masks that require high level manufacturing accuracy. Recently, aggressive OPC methods such as ILT have significantly increased the complexity of mask data. This impacts Mask Data Preparation’s (MDP) processing time due to large mask data volumes. By its nature, MB-MPC process is quite time-consuming since it needs to perform complex calculations repeatedly, and so it takes the largest part of the total MDP time. This puts high pressure on turn-around time (TAT) reduction without losing accuracy and necessitates the need to develop algorithms that can operate on tight TAT budgets. Pattern Matching (PM) approach could be used to mitigate high processing times of MB-MPC by leveraging inherent repetitiveness of real-world mask data. Since a pattern simulation result is influenced by all patterns located within the mask model radius, to consider one pattern as a repetition of another, the central pattern as well as the neighborhood must match. This method is called Neighborhood Pattern Matching (NPM). In this paper, we evaluate the effectiveness of NPM when applied to the MB-MPC software developed by Synopsys. First, we introduce the fundamental concepts of NPM. Then we validate the algorithm with test patterns to evaluate its behavior. Finally, we measure processing time with several types of device patterns and confirm how NPM can reduce MPC calculation time on real mask data.

Paper Details

Date Published: 26 September 2019
PDF: 8 pages
Proc. SPIE 11148, Photomask Technology 2019, 111481K (26 September 2019); doi: 10.1117/12.2538626
Show Author Affiliations
Kohei Yanagisawa, Toppan Printing Co., Ltd. (Japan)
Kota Kobayashi, Toppan Printing Co., Ltd. (Japan)
Kiyoshi Kageyama, Toppan Printing Co., Ltd. (Japan)
Sebastian Munoz, Synopsys, Inc. (United States)
Fabian Saso, Synopsys, Inc. (United States)
Parikshit Kulkarni, Synopsys, Inc. (United States)
John Valadez, Synopsys, Inc. (United States)
Kokoro Kato, Synopsys, Inc. (United States)


Published in SPIE Proceedings Vol. 11148:
Photomask Technology 2019
Jed H. Rankin; Moshe E. Preil, Editor(s)

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