Methodology and practical application of an ArF resist model calibration

Ziebold, Ralf; Küchler, Bernd; Nölscher, Christoph; Robiger, Martin; Elian, Klaus; Tollkuhn, Bernd

doi:10.1117/12.536343

Cited by 1 publication

(1 citation statement)

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“…The target resist dimension is 81 nm. MEF data were collected for this structure to reduce uncertainty in the mask dimensions [6]. Using the dense FE to anchor focus and dose, proximity data were generated for various lines and space spanning a nominal line width of 120 nm to 240 nm.…”

Section: -2 Data Collectionmentioning

confidence: 99%

A calibrated photoresist model for pattern prediction

Hung

Liao

Shih

et al. 2008

Advances in Resist Materials and Processing Technology XXV

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Aerial image metrics and non-rigorous resist modeling have been used in computational lithography to estimate the resist CD on the wafer, with varying degrees of success. However, the complex illumination and optical-proximity correction strategies used at the 90 nm node and below demand accurate simulation at the wafer level, allowing engineers to make informed decisions when optimizing the process. A calibrated, rigorous model simulating the physical-chemistry of exposure, reaction-diffusion and development can produce highly-accurate predictions, even when extrapolated. The advantage of the rigorous physical approach is that simulations retain predictive accuracy even when the experimental conditions are modified.In this work, the rigorous physical resist model available in PROLITH is calibrated for a commercially available ArF resist using top-down CDSEM data, including focus-exposure matrices, CD vs. mask pitch and mask-error enhancement data. All calibration data consist of 1D mask structure e.g. lines and spaces. It will be shown that the resulting resist model, while calibrated using 1D mask structures, is able to accurately predict the performance of 2D mask structures with an area fidelity metric of 98.34% under the illumination conditions used for calibration. It will also be shown that, additionally, the calibrated resist model is able to predict 2D patterns robustly under alternate illumination schemes without the need for re-calibration. The resist model accurately describes the experimental results, validating the extrapolated model predictions. Accurate, calibrated models can be used to improve the process with much less impact on time and resources than empirical methods i.e. wafer fabrication and inspection.

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Section: -2 Data Collectionmentioning

confidence: 99%