Advanced module-based approach to effective CD prediction of sub-100nm patterns

Jangho Shin; Insung Kim; Chan Hwang; Dong-Woon Park; Sang-Gyun Woo; Han-Ku Cho; Woo-Sung Han; Joo-Tae Moon

doi:10.1117/12.536345

29 April 2004 Advanced module-based approach to effective CD prediction of sub-100nm patterns

Jangho Shin, Insung Kim, Chan Hwang, Dong-Woon Park, Sang-Gyun Woo, Han-Ku Cho, Woo-Sung Han, Joo-Tae Moon

Proceedings Volume 5378, Data Analysis and Modeling for Process Control; (2004) https://doi.org/10.1117/12.536345
Event: Microlithography 2004, 2004, Santa Clara, California, United States

Abstract

In this article, an advanced module-based approach is introduced to simulate sub-100 nm patterns. Topography (TOPO), an in-house lithography simulator, consists of four basic modules: i) illumination, ii) mask, iii) imaging, and iv) resist. Since TOPO is module-based, it is convenient for user specific applications. The input parameter of illumination module is pupil intensity profile, which is measured using the transmission image sensor of ASML. In the mask kernel, mask corner rounding effect is considered while imaging module takes care of lens aberration and flare problems. Finally, the resist module uses Gaussian convolution model with the trade-off in mind between accuracy of full resist model and speed of Gaussian convolution model. As an application example, an iso-dense bias (ID bias) fitting is implemented for an ArF resist to image sub-100 nm patterns. Simulation results show that the fitting error meets the prediction accuracy target of International Technology Roadmap for Semiconductors 2002. The advanced module-based model using aerial image with measured pupil intensity profile and Gaussian convolution seems to be an effective way for the CD prediction of sub-100 nm patterns.

Citation Download Citation

Jangho Shin, Insung Kim, Chan Hwang, Dong-Woon Park, Sang-Gyun Woo, Han-Ku Cho, Woo-Sung Han, and Joo-Tae Moon "Advanced module-based approach to effective CD prediction of sub-100nm patterns", Proc. SPIE 5378, Data Analysis and Modeling for Process Control, (29 April 2004); https://doi.org/10.1117/12.536345

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