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Journal of Micro/Nanolithography, MEMS, and MOEMS • Open Access

Fast pixel-based optical proximity correction based on nonparametric kernel regression
Author(s): Xu Ma; Bingliang Wu; Zhiyang Song; Shangliang Jiang; Yanqiu Li

Paper Abstract

Optical proximity correction (OPC) is a resolution enhancement technique extensively used in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the layout is divided into small pixels, which are then iteratively modified until the simulated print image on the wafer matches the desired pattern. However, the increasing complexity and size of modern integrated circuits make PBOPC techniques quite computationally intensive. This paper focuses on developing a practical and efficient PBOPC algorithm based on a nonparametric kernel regression, a well-known technique in machine learning. Specifically, we estimate the OPC patterns based on the geometric characteristics of the original layout corresponding to the same region and a series of training examples. Experimental results on metal layers show that our proposed approach significantly improves the speed of a current professional PBOPC software by a factor of 2 to 3, and may further reduce the mask complexity.

Paper Details

Date Published: 3 November 2014
PDF: 11 pages
J. Micro/Nanolith. 13(4) 043007 doi: 10.1117/1.JMM.13.4.043007
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 13, Issue 4
Show Author Affiliations
Xu Ma, Beijing Institute of Technology (China)
Bingliang Wu, Beijing Institute of Technology (China)
Zhiyang Song, Beijing Institute of Technology (China)
Shangliang Jiang, Univ. of California, Berkeley (United States)
Yanqiu Li, Beijing Institute of Technology (China)


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