Data augmentation in extreme ultraviolet lithography simulation using convolutional neural network

Background: In the previous work, we developed a convolutional neural network (CNN), which reproduces the results of the rigorous electromagnetic (EM) simulations in a small mask area. The prediction time of CNN was 5000 times faster than the calculation time of EM simulation. We trained the CNN using 200,000 data, which were the results of EM simulation. Although the prediction time of CNN was very short, it took a long time to build a huge amount of the training data. Especially when we enlarge the mask area, the calculation time to prepare the training data becomes unacceptably long.