New process proximity correction using neural network in spacer patterning technology

Fumiharu Nakajima; Toshiya Kotani; Satoshi Tanaka; Masafumi Asano; Soichi Inoue

doi:10.1117/12.813614

16 March 2009 New process proximity correction using neural network in spacer patterning technology

Fumiharu Nakajima, Toshiya Kotani, Satoshi Tanaka, Masafumi Asano, Soichi Inoue

Proceedings Volume 7274, Optical Microlithography XXII; 72740J (2009) https://doi.org/10.1117/12.813614
Event: SPIE Advanced Lithography, 2009, San Jose, California, United States

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.

Citation Download Citation

Fumiharu Nakajima, Toshiya Kotani, Satoshi Tanaka, Masafumi Asano, and Soichi Inoue "New process proximity correction using neural network in spacer patterning technology", Proc. SPIE 7274, Optical Microlithography XXII, 72740J (16 March 2009); https://doi.org/10.1117/12.813614

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