Mohamed Bahnas and Mohamed Al-Imam
Mentor Graphics

Optical proximity correction (OPC) models have been improving their accuracy over the years by modeling more error sources in lithographic systems, but model calibration techniques are improving at a slower pace. One area of modeling calibration that has garnered little interest is the statistical variance of the calibration data set.

This paper presents a feasibility study for treatment of data variance during model calibration. Its particular approach was developed to improve the model fitness for primary out-of-specification features present in the calibration test pattern by performing small manipulations of the measured data combined with data weighting during the model calibration process.