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Proceedings Paper

New process proximity correction using neural network in spacer patterning technology
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Paper Abstract

A neural network (NN)-based approach with a lumped model is found to be much more promising to predict process proximity effects (PPEs) caused through space patterning processes than a conventional tandem-based approach with a consecutive physical model. The NN-based lumped approach can improve PPE prediction accuracy by 25% compared to the conventional tandem-based approach, subject to the same workload of experimental data acquisition, and reach the specification of PPE residual in 3x nm node with smaller amounts of data volume than any other approach. Process proximity correction scheme using the NN-based lumped model built for 3x nm node can achieve the expected correction accuracy for various kinds of one-dimensional patterns. It is anticipated that the NN-based lumped PPE prediction model will greatly improve the prediction and/or correction accuracy in the space patterning technology process for 3x nm node and beyond.

Paper Details

Date Published: 16 March 2009
PDF: 7 pages
Proc. SPIE 7274, Optical Microlithography XXII, 72740J (16 March 2009); doi: 10.1117/12.813614
Show Author Affiliations
Fumiharu Nakajima, Toshiba Corp. Semiconductor Co. (Japan)
Toshiya Kotani, Toshiba Corp. Semiconductor Co. (Japan)
Satoshi Tanaka, Toshiba Corp. Semiconductor Co. (Japan)
Masafumi Asano, Toshiba Corp. Semiconductor Co. (Japan)
Soichi Inoue, Toshiba Corp. Semiconductor Co. (Japan)


Published in SPIE Proceedings Vol. 7274:
Optical Microlithography XXII
Harry J. Levinson; Mircea V. Dusa, Editor(s)

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