Synthetic data generation overcomes IP concerns in hardware assurance

Researchers have developed a novel method for generating synthetic datasets to train hardware assurance models, addressing challenges of data scarcity and intellectual property (IP) confidentiality. The pipeline uses StyleGAN to create diverse hardware layout masks and Pix2PixHD to translate these into realistic SEM images. A key finding is that models trained solely on this synthetic data achieve successful "sim-to-real" transfer and outperform models trained on limited real datasets, while also mitigating IP exposure risks. AI

IMPACT This approach could enable more robust and secure hardware verification by overcoming data limitations and protecting proprietary designs.

RANK_REASON The cluster contains an academic paper detailing a new methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

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• Pix2PixHD
• StyleGAN

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