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Journal of Micro/Nanolithography, MEMS, and MOEMS

New method of optimizing writing parameters in electron beam lithography systems for throughput improvement considering patterning fidelity constraints
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Paper Abstract

Low-energy electron beam lithography is one of the promising next-generation lithography technology solutions for the 21-nm half-pitch node and beyond because of fewer proximity effects, higher resist sensitivity, and less substrate damage compared with high-energy electron beam lithography. To achieve high-throughput manufacturing, low-energy electron beam lithography systems with writing parameters of larger beam size, larger grid size, and lower dosage are preferred. However, electron shot noise can significantly increase critical dimension deviation and line edge roughness. Its influence on patterning prediction accuracy becomes nonnegligible. To effectively maximize throughput while meeting patterning fidelity requirements according to the International Technology Roadmap for Semiconductors, a new method is proposed in this work that utilizes a new patterning prediction algorithm to rigorously characterize the patterning variability caused by the shot noise and a mathematical optimization algorithm to determine optimal writing parameters. The new patterning prediction algorithm can achieve a proper trade-off between computational effort and patterning prediction accuracy. Effectiveness of the new method is demonstrated on a static random-access memory circuit. The corresponding electrical performance is analyzed by using a gate-slicing technique and publicly available transistor models. Numerical results show that a significant improvement in the static noise margin can be achieved.

Paper Details

Date Published: 22 August 2012
PDF: 13 pages
J. Micro/Nanolith. 11(3) 033007 doi: 10.1117/1.JMM.11.3.033007
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 11, Issue 3
Show Author Affiliations
Hoi-Tou Ng, National Taiwan Univ. (Taiwan)
Yu-Tian Shen, National Taiwan Univ. (Taiwan)
Sheng-Yung Chen, National Taiwan Univ. (Taiwan)
Chun-Hung Liu, National Taiwan Univ. (Taiwan)
Philip C. W. Ng, National Taiwan Univ. (Taiwan)
Kuen-Yu Tsai, National Taiwan Univ. (Taiwan)

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