Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection
Since the scaling of advanced technology nodes is pushing to its physical limit, lithography hotspot detection (LHD) has become more significant than ever in design for manufacturability. Recently, machine learning techniques have been deployed to greatly reduce simulation time for hotspot detection, but high-quality data are required to build a model. Many design companies do not have enough high-quality data and are hesitant to share it for fear of intellectual property theft or model ineffectiveness. Furthermore, using locally trained models with limited and similar data can lead to overfitting and a lack of generalization and robustness when applied to new designs. In this article, we propose a heterogeneous federated learning framework for LHD that can address the aforementioned issues. Our framework can overcome the challenges of nonindependent and identically distributed data and heterogeneous communication, ensuring high performance and good convergence in various scenarios. The proposed framework creates a more robust centralized global submodel through heterogeneous knowledge sharing while keeping local data private. Then, it combines the global submodel with a local submodel for better adaptation to local data heterogeneity. Our experimental results show that the proposed framework outperforms other state-of-the-art methods.
Duke Scholars
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Related Subject Headings
- Computer Hardware & Architecture
- 4607 Graphics, augmented reality and games
- 4009 Electronics, sensors and digital hardware
- 1006 Computer Hardware
- 0906 Electrical and Electronic Engineering
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Computer Hardware & Architecture
- 4607 Graphics, augmented reality and games
- 4009 Electronics, sensors and digital hardware
- 1006 Computer Hardware
- 0906 Electrical and Electronic Engineering