Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation

Hardware assurance (HA) requires analyzing scanning electron microscopy (SEM) images of integrated circuits (ICs) to verify their integrity. However, this process is hindered by two major obstacles: the scarcity of large, annotated datasets and the extreme sensitivity of proprietary intellectual property (IP). The authors investigate whether a synthetic data generation pipeline can overcome these barriers while ensuring that the resulting models do not leak sensitive design information.

The researchers propose a two-stage generative framework. First, they use a StyleGAN to learn the distribution of hardware layout masks from a small set of real examples, generating novel, macroscopically varied synthetic masks. Second, they employ a Pix2PixHD conditional GAN to translate these synthetic masks into realistic SEM images that capture authentic textures and noise. This synthetic dataset is then used to train a U-Net segmentation model. The privacy of the original design is protected because the synthetic layouts are structurally novel and do not replicate the specific proprietary routing of the source IC.

The study demonstrates that a U-Net model trained solely on synthetic data successfully performs "sim-to-real" transfer, achieving high accuracy on real SEM images. Notably, this synthetic-trained model outperforms a baseline U-Net trained on a limited real-world dataset. The authors validate the privacy-preserving nature of the pipeline through PCA, showing that synthetic and real datasets form distinct, non-overlapping clusters, confirming that the synthetic data does not simply memorize or reconstruct the original proprietary layouts.

This work provides a scalable, secure solution for hardware assurance. By enabling the sharing of trained segmentation models without the risk of exposing sensitive IP, the pipeline allows untrusted third-party facilities or external researchers to perform automated hardware verification. This approach effectively decouples the need for high-performance computer vision from the risks associated with handling proprietary semiconductor design data.