Paper
26 March 2008 A comprehensive resist model for the prediction of line-edge roughness material and process dependencies in optical lithography
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Abstract
The reduction of semiconductor device dimensions necessitates, amongst others, a reduction of the line-edge roughness (LER) of the lithographically patterned device components. Experimentally, the impact of many process and material parameters on resist LER has been demonstrated. The impact of some parameters on LER has also been described quantitatively. This paper presents a mesoscopic (i.e., discrete and stochastic) modeling approach including all exposure, post-exposure bake (PEB), and development related parameters and their impact on LER. This allows a prediction of the resulting resist profiles including average dimensions as well as LER. The mesoscopic models are applied for simulating the impact of aerial image contrast, acid diffusion length, and quencher base concentration on LER. The results are compared to experimental data. After this validation of the models, they are applied for LER optimization. The optimum combination of acid and base diffusion length is identified for resist formulations with various levels of base concentration. While the impact of acid diffusion length is already known in principle, it is shown in this paper for the first time how the optimum acid diffusion length depends crucially on base di®usion length and initial base concentration of the resist.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Schnattinger and Andreas Erdmann "A comprehensive resist model for the prediction of line-edge roughness material and process dependencies in optical lithography", Proc. SPIE 6923, Advances in Resist Materials and Processing Technology XXV, 69230R (26 March 2008); https://doi.org/10.1117/12.772507
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Cited by 14 scholarly publications.
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KEYWORDS
Line edge roughness

Polymers

Diffusion

Molecules

Materials processing

Photoresist processing

Stochastic processes

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