A method for calibrating a curvature-based pre-bias model for advanced mask process correction applications

Rachit Sharma; Mary Zuo; Ingo Bork; Archana Rajagopalan; Malavika Sharma; Kushlendra Mishra

doi:10.1117/12.2641760

1 December 2022 A method for calibrating a curvature-based pre-bias model for advanced mask process correction applications

Rachit Sharma, Mary Zuo, Ingo Bork, Archana Rajagopalan, Malavika Sharma, Kushlendra Mishra

Author Affiliations +

Proceedings Volume 12293, Photomask Technology 2022; 1229309 (2022) https://doi.org/10.1117/12.2641760
Event: SPIE Photomask Technology + EUV Lithography, 2022, Monterey, California, United States

Abstract

Techniques such as Inverse Lithography Technology (ILT) generate complex curvilinear mask shapes which require advanced features and algorithms for efficient execution of the mask data preparation step. For curvilinear mask shapes, there is a strong correlation between the curvature at the point of interest and the bias required in CLMPC correction. Curvature kernels can be used to take advantage of this correlation and can be used to apply a pre-bias to the CLMPC target layer to move it closer to the ideal CLMPC output. Typical ILT results, which serve as input to CLMPC, contain a large variation of local curvatures and a key challenge for effectively applying curvature-based pre-bias is to build a single pre-bias model that works effectively for varying layout curvatures. This paper presents a novel method to calibrate a comprehensive pre-bias model for a given mask process using contour-based modeling techniques. The results demonstrate that a pre-bias model calibrated with this method can offer significant performance benefits and play a crucial role in the development of efficient and accurate CLMPC flows for advanced mask process applications.

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Rachit Sharma, Mary Zuo, Ingo Bork, Archana Rajagopalan, Malavika Sharma, and Kushlendra Mishra "A method for calibrating a curvature-based pre-bias model for advanced mask process correction applications", Proc. SPIE 12293, Photomask Technology 2022, 1229309 (1 December 2022); https://doi.org/10.1117/12.2641760

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