  Lithography modeling is a crucial problem in chip design to ensure a chip
design mask is manufacturable. It requires rigorous simulations of optical and
chemical models that are computationally expensive. Recent developments in
machine learning have provided alternative solutions in replacing the
time-consuming lithography simulations with deep neural networks. However, the
considerable accuracy drop still impedes its industrial adoption. Most
importantly, the quality and quantity of the training dataset directly affect
the model performance. To tackle this problem, we propose a litho-aware data
augmentation (LADA) framework to resolve the dilemma of limited data and
improve the machine learning model performance. First, we pretrain the neural
networks for lithography modeling and a gradient-friendly StyleGAN2 generator.
We then perform adversarial active sampling to generate informative and
synthetic in-distribution mask designs. These synthetic mask images will
augment the original limited training dataset used to finetune the lithography
model for improved performance. Experimental results demonstrate that LADA can
successfully exploits the neural network capacity by narrowing down the
performance gap between the training and testing data instances.
