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

Techniques like inverse lithography technology (ILT) generate complex curvilinear mask shapes that 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 curvilinear mask process correction (CLMPC). 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.