  Lithography simulation is one of the key steps in physical verification,
enabled by the substantial optical and resist models. A resist model bridges
the aerial image simulation to printed patterns. While the effectiveness of
learning-based solutions for resist modeling has been demonstrated, they are
considerably data-demanding. Meanwhile, a set of manufactured data for a
specific lithography configuration is only valid for the training of one single
model, indicating low data efficiency. Due to the complexity of the
manufacturing process, obtaining enough data for acceptable accuracy becomes
very expensive in terms of both time and cost, especially during the evolution
of technology generations when the design space is intensively explored. In
this work, we propose a new resist modeling framework for contact layers,
utilizing existing data from old technology nodes and active selection of data
in a target technology node, to reduce the amount of data required from the
target lithography configuration. Our framework based on transfer learning and
active learning techniques is effective within a competitive range of accuracy,
i.e., 3-10X reduction on the amount of training data with comparable accuracy
to the state-of-the-art learning approach.
