Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation
As technology scaling is approaching its physical limit, lithography hotspot detection has become an essential task in design for manufacturability. Although the deployment of machine learning in hotspot detection is found to save significant simulation time, such methods typically demand non-trivial quality data to build the model. While most design houses are actually short of quality data, they are also unwilling to directly share such layout related data to build a unified model due to the concerns on IP protection and model effectiveness. On the other hand, with data homogeneity and insufficiency within each design house, the locally trained models can be easily over-fitted, losing generalization ability and robustness when applying to the new designs. In this paper, we propose a heterogeneous federated learning framework for lithography hotspot detection that can address the aforementioned issues. The framework can build a more robust centralized global sub-model through heterogeneous knowledge sharing while keeping local data private. Then the global sub-model can be combined with a local submodel 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 good convergence in various scenarios.
