The continuous reduction of device dimensions and densities of integrated circuits increases the demand for accurate
process window models used in optical proximity correction. Beamfocus and dose are process parameters that have
significant contribution to the overall critical feature dimension error budget. The increased number of process
conditions adds to the model calibration time since a new optical model needs to be generated for each focus condition.
This study shows how several techniques can reduce the calibration time by appropriate selection of process conditions
and features while maintaining good accuracy. Experimental data is used to calibrate models using a reduced set of data.
The resulting model is compared with the model calibrated using the full set of data. The results show that using a
reduced set of process conditions and using process sensitive features can yield a model as accurate as the model
calibrated using the full set but in a shorter amount of time.