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Predictive linewidth roughness and CDU simulation using a calibrated physical stochastic resist model Stewart A. Robertsona, John J. Biaforea, Mark D. Smitha, Michael T. Reillyb, Jerome Wandellb. a - KLA-Tencor Corp., FINLE Division, Austin, TX, USA. b - DOW Electronic Materials, Marlborough, MA, USA. ABSTRACT A recently developed stochastic resist model, implemented in the PROLITH X3.1 lithography simulation tool, is fitted to experimental data for a commercially available immersion ArF photoresist, EPIC 2013 (Dow Electronic Materials). Calibration is performed using the mean CD and LWR values through focus and dose for three line/space features of varying pitch (dense, semi-dense and isolated). An unweighted Root Mean Squared Error (RMSE) of approximately 1.6 nm is observed when the calibrated model is compared to the experimental data. Although the model is calibrated only to mean CD and LWR values, it is able to accurately predict highly accurate CDU distributions at fixed focus and dose conditions for 1D and 2D (line end shortening) pattern. It is also shown how the stochastic model can be used to describe the bridging behavior often observed at marginal focus and exposure conditions.

Keywords: Line Edge Roughness (LER), LineWidth Roughness (LWR), Stochastic lithographic modeling, PROLITH 1. INTRODUCTION

Virtually all lithography simulation used in the semiconductor industry relies on the continuum, or meanfield, approximation. Such models assume that the exposing illumination can be treated as a series of interfering plane waves and that the distribution of chemical components within the photoresist (PAG, photoacid, quencher etc) is completely homogeneous and can vary continuously. Although this approach ignores statistical effects, due to the quantization of light into photons and the fact that resist components are discrete molecules, it has been used successfully for decades to predict core lithographic behaviors. However, as lithography approaches its fundamental physical limits, phenomena driven by quantized statistical processes, such as line-edge roughness, contact hole circularity and CD distribution, are becoming increasingly important. If virtual lithography is to help address these new industry challenges, then simulation tools need to, at least partially, transition from their current deterministic domain into a probabilistic one.

Advances in Resist Materials and Processing Technology XXVII, edited by Robert D. Allen, Mark H. Somervell, Proc. of SPIE Vol. 7639, 763934 路 漏 2010 SPIE 路 CCC code: 0277-786X/10/$18 路 doi: 10.1117/12.846539

Proc. of SPIE Vol. 7639 763934-1 Downloaded from SPIE Digital Library on 26 Mar 2010 to 192.146.1.254. Terms of Use: http://spiedl.org/terms


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