PRICING: Privacy-Preserving Circuit Data Sharing Framework for Lithographic Hotspot Detection
To apply machine learning (ML) techniques for electronic design automation (EDA), training models on diverse datasets is essential for model reliability and generalizability, especially when applied to modern circuits. However, data availability remains a severe issue as circuit data is typically kept confidential within each data provider due to the difficulty of secure data sharing. This problem has impeded the development of ML for EDA in both industry and academia and has never been well addressed. To facilitate model development, enabling secure data sharing among various data providers is needed. To this end, we propose PRICING, a privacy-preserving circuit data sharing framework. This is the first exploration to (1) investigate the secure data sharing problem in EDA and (2) generate protected circuit features that hide important circuit information while preserving sufficient information for a well-known EDA application, lithographic hotspot detection. Our results demonstrate that our approach successfully protects raw circuit features, providing 55% superior protection over existing state-of-the-art techniques in computer vision. Moreover, models trained with our protected data achieve up to 48% higher accuracy than models trained with limited raw data. This shows the effectiveness of PRICING in enhancing model development for EDA.
