  As technology scaling is approaching the physical limit, lithography hotspot
detection has become an essential task in design for manufacturability. While
the deployment of pattern matching or machine learning in hotspot detection can
help save significant simulation time, such methods typically demand for
non-trivial quality data to build the model, which most design houses are short
of. Moreover, the design houses are also unwilling to directly share such data
with the other houses to build a unified model, which can be ineffective for
the design house with unique design patterns due to data insufficiency. On the
other hand, with data homogeneity in each design house, the locally trained
models can be easily over-fitted, losing generalization ability and robustness.
In this paper, we propose a heterogeneous federated learning framework for
lithography hotspot detection that can address the aforementioned issues. On
one hand, the framework can build a more robust centralized global sub-model
through heterogeneous knowledge sharing while keeping local data private. On
the other hand, the global sub-model can be combined with a local sub-model to
better adapt to local data heterogeneity. The experimental results show that
the proposed framework can overcome the challenge of non-independent and
identically distributed (non-IID) data and heterogeneous communication to
achieve very high performance in comparison to other state-of-the-art methods
while guaranteeing a good convergence rate in various scenarios.
