Data efficient deep learning for imaging with novel EUV mask absorbers

Abstract

The potential of deep learning as a supplement for image simulations to allow for more efficient modeling of new lithographic configurations has been explored in recent years. A routine challenge with deep learning solutions is their inherent data inefficiency. This work details a deep learning model capable of predicting aerial images for different mask absorbers and illumination settings. We expand on this model by investigating its accuracy potential and data efficiency. This investigation provides insights into the amounts of training data required to achieve the optimum accuracy for different absorber stacks. A significant variance in data requirements and achievable accuracy across different absorbers is observed. The observed trends indicate that the amount of training data required to train the model is directly correlated to the severity of the mask-3D effects of the absorber. This work presents a method that can improve the data efficiency of this predictive model without compromising the accuracy for novel absorbers or new lithographic configurations.