An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design. (arXiv:2210.15765v1 [cs.LG])

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 …

arxiv augmentation chip chip design data design framework sampling