  Lithography, transferring chip design masks to the silicon wafer, is the most
important phase in modern semiconductor manufacturing flow. Due to the
limitations of lithography systems, Extensive design optimizations are required
to tackle the design and silicon mismatch. Inverse lithography technology (ILT)
is one of the promising solutions to perform pre-fabrication optimization,
termed mask optimization. Because of mask optimization problems' constrained
non-convexity, numerical ILT solvers rely heavily on good initialization to
avoid getting stuck on sub-optimal solutions. Machine learning (ML) techniques
are hence proposed to generate mask initialization for ILT solvers with
one-shot inference, targeting faster and better convergence during ILT. This
paper addresses the question of \textit{whether ML models can directly generate
high-quality optimized masks without engaging ILT solvers in the loop}. We
propose an implicit learning ILT framework: ILILT, which leverages the implicit
layer learning method and lithography-conditioned inputs to ground the model.
Trained to understand the ILT optimization procedure, ILILT can outperform the
state-of-the-art machine learning solutions, significantly improving efficiency
and quality.
