Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation

2026-08-10

AI Summary

This work addresses the scarcity of high-quality scanning electron microscope (SEM) training data in hardware verification, which stems from time-intensive acquisition and intellectual property constraints. To overcome this challenge, the authors propose a privacy-preserving two-stage generative framework: first, StyleGAN is employed to synthesize macro-layout masks with novel structural configurations; subsequently, a conditional GAN (Pix2PixHD) translates these masks into photorealistic SEM images. The approach generates visually realistic and topologically diverse synthetic data without exposing any original wiring information, thereby enabling secure simulation-to-reality transfer while resisting gradient inversion and membership inference attacks. Notably, segmentation models trained exclusively on this synthetic data not only generalize effectively to real SEM images but also outperform baselines trained on limited real-world data.

Abstract

Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets required for automated analysis is impeded by time-intensive acquisition and strict intellectual property (IP) constraints on proprietary designs. We propose a privacy-preserving pipeline that secures IP by heavily distorting the functional design while generating a visually realistic synthetic dataset from a small set of initial examples. A StyleGAN first learns the distribution of hardware layout masks to generate novel, macroscopically varied structures. Subsequently, a conditional GAN (Pix2PixHD) translates these masks into realistic SEM images that preserve authentic textures and noise. The primary finding of this work is that a segmentation model trained exclusively on this synthetic data not only demonstrates a successful "sim-to-real" transfer to real images but also outperforms a baseline model trained on the limited real dataset. Because the underlying synthetic layouts are demonstrably novel and reproduce none of the specific proprietary routing of the original design, deploying the final segmentation model mitigates the risk of exposing sensitive IP to attacks like gradient inversion and membership inference, providing a highly secure, high-performance solution for hardware assurance.

Problem

• Hardware assurance
• Data scarcity
• Confidentiality
• Intellectual property
• SEM imaging

Innovation

• synthetic data generation
• privacy-preserving AI
• StyleGAN
• conditional GAN
• hardware assurance