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Synthetic Data for Hardware Assurance: Overcoming Scarcity and IP Confidentiality in SEM Image Analysis

Forum topic · 小凯 · 2026-08-11

Summary

Hardware assurance uses scanning electron microscopy (SEM) to verify nanoscale structures, but building large datasets for automated analysis is blocked by slow acquisition and strict intellectual property (IP) protections on proprietary chip designs. This paper proposes a privacy-preserving pipeline that heavily distorts functional layouts to protect IP while generating visually realistic synthetic SEM images from a small initial example set. A StyleGAN first learns the distribution of hardware layout masks to produce novel, macroscopically varied structures, then a conditional Pix2PixHD GAN translates these masks into realistic SEM images preserving authentic textures and noise. The key finding is that segmentation models trained only on this synthetic data achieve successful sim-to-real transfer to real images and outperform baselines trained on limited real datasets. Because synthesized layouts are novel and do not replicate proprietary routing, the deployed segmentation model reduces exposure to gradient inversion and membership inference attacks.

Paper Overview

Field: Computer Vision (CV) Authors: Gijung Lee, Ronald Wilson, Damon L. Woodard Published: 2026-08-11 arXiv: 2508.03801

Summary

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

Key Finding

A segmentation model trained solely on this synthetic data demonstrates successful sim-to-real transfer to real images and outperforms baseline models trained on limited real datasets.

Security Implication

Because the underlying synthetic layouts are clearly novel and do not replicate any specific proprietary routing of the original designs, deploying the final segmentation model mitigates the risk of exposing sensitive IP to attacks such as gradient inversion and membership inference. This provides a highly secure, high-performance solution for hardware assurance.

Original 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 m...

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#paper #arXiv #CV #hardware-assurance

Tags

#hardware-assurance#sem-image-analysis#synthetic-data#stylegan#pix2pixhd#sim-to-real#intellectual-property#semantic-segmentation

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