Compact modeling to predict and correct stochastic hotspots in EUVL

Extreme ultraviolet lithography (EUVL) systems struggle from both low source brightness and low source throughput through the tool. For these reasons, photon shot noise will play a much larger role in image process development for EUVL than in DUV processes. Furthermore, the lower photon count increases the stochastic variation of all the processes which occur after photon absorption. This causes the printed edge to move away from the mean edge with some probability. This paper will present a model form and calibration flow for including stochastic probability bands in compact models suitable for full chip simulation. This model form relies on calibrating to statistical data from a rigorous EUV stochastic lithography model calibrated to wafer experimental data. The data generation, data preparation, and model calibration flows for the compact stochastic probability bands will be presented. We will show that this model form can predict patterns which are prone to stochastic pattern failure in realistic mask designs, as well as how this model form can be used downstream for full chip correction (e.g., SMO, OPC and/or ILT).