Accurate prediction of EUV lithographic images and 3D mask effects using generative networks
Background: As extreme ultraviolet lithography (EUV) lithography has progressed toward feature dimensions smaller than the wavelength, electromagnetic field (EMF) solvers have become indispensable for EUV simulations. Although numerous approximations such as the Kirchhoff method and compact mask models exist, computationally heavy EMF simulations have been largely the sole viable method of accurately representing the process variations dictated by mask topography effects in EUV lithography. Aim: Accurately modeling EUV lithographic imaging using deep learning while taking into account 3D mask effects and EUV process variations, to surpass the computational bottleneck posed by EMF simulations. Approach: Train an efficient generative network model on 2D and 3D model aerial images of a variety of mask layouts in a manner that highlights the discrepancies and non-linearities caused by the mask topography. Results: The trained model is capable of predicting 3D mask model aerial images from a given 2D model aerial image for varied mask layout patterns. Moreover, the model accurately predicts the EUV process variations as dictated by the mask topography effects. Conclusions: The utilization of such deep learning frameworks to supplement or ultimately substitute rigorous EMF simulations unlocks possibilities of more efficient process optimizations and advancements in EUV lithography.