Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic SEM Image Generation

Forum topic · 小凯 · 2026-08-12

Summary

A paper by Gijung Lee, Ronald Wilson, and Damon L. Woodard (arXiv:2508.05150) addresses data scarcity and intellectual property confidentiality in hardware assurance. SEM-based verification of nanoscale structures requires large datasets that are costly to acquire and restricted by IP constraints on proprietary designs. The authors propose a privacy-preserving pipeline: StyleGAN learns the distribution of hardware layout masks to generate novel, macroscopically varied structures, then Pix2PixHD translates these masks into realistic SEM images preserving authentic textures and noise. The key finding is that a segmentation model trained solely on this synthetic data achieves sim-to-real transfer and outperforms baselines trained on limited real data. Since the synthetic layouts are novel and do not copy proprietary routing, deploying the model reduces exposure to attacks like gradient inversion and membership inference, offering a secure, high-performance solution for hardware verification.

Paper Overview

Field: Computer Vision Authors: Gijung Lee, Ronald Wilson, Damon L. Woodard Published: 2026-08-12 arXiv: 2508.05150

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. The authors 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.

Method

1. A StyleGAN first learns the distribution of hardware layout masks to generate novel, macroscopically varied structures. 2. A conditional GAN (Pix2PixHD) then translates these masks into realistic SEM images that preserve authentic textures and noise.

Key Finding

The primary finding of this work is that a segmentation model trained only on this synthetic data not only achieves successful 'sim-to-real' transfer to real images, but also outperforms baselines trained on limited real datasets. Because the underlying synthetic layouts are clearly novel and do not replicate any specific proprietary routing of original designs, deploying the final segmentation model effectively reduces the risk of exposing sensitive IP to attacks such as gradient inversion and membership inference, providing a highly secure, high-performance solution for hardware verification.

---

*Auto-collected on 2026-08-12*