  Machine learning techniques have been extensively studied for mask
optimization problems, aiming at better mask printability, shorter turnaround
time, better mask manufacturability, and so on. However, most of these
researches are focusing on the initial solution generation of small design
regions. To further realize the potential of machine learning techniques on
mask optimization tasks, we present a Convolutional Fourier Neural Operator
(CFNO) that can efficiently learn layout tile dependencies and hence promise
stitch-less large-scale mask optimization with the limited intervention of
legacy tools. We discover the possibility of litho-guided self-training (LGST)
through a trained machine learning model when solving non-convex optimization
problems, which allows iterative model and dataset update and brings
significant model performance improvement. Experimental results show that, for
the first time, our machine learning-based framework outperforms
state-of-the-art academic numerical mask optimizers with an order of magnitude
speedup.
