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

Forum topic · 小凯 · 2026-08-11

Summary

Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale chip structures, but building large, high-quality datasets for automated analysis is hindered by time-intensive image acquisition and strict intellectual property (IP) restrictions on proprietary designs. Researchers propose a privacy-preserving pipeline that generates visually realistic synthetic SEM data from only a small set of initial examples while heavily distorting functional designs to protect IP. A StyleGAN first learns the distribution of hardware layout masks to produce novel, macroscopically varied structures; a conditional GAN (Pix2PixHD) then translates these masks into realistic SEM images retaining authentic textures and noise. The key finding is that a segmentation model trained solely on this synthetic data not only achieves successful sim-to-real transfer to real SEM images but outperforms baselines trained on limited real data. Because the underlying synthetic layouts are demonstrably novel and do not replicate any specific proprietary routing of original designs, deploying the final segmentation model reduces the risk of exposing sensitive IP to attacks such as gradient inversion and membership inference, offering a secure, high-performance solution for hardware assurance.

Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation

Field: Computer Vision Authors: Gijung Lee, Ronald Wilson, Damon L. Woodard arXiv: 2508.03801

Overview

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. This work proposes 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. Layout generation: A StyleGAN first learns the distribution of hardware layout masks to generate novel, macroscopically varied structures. 2. Mask-to-SEM translation: A conditional GAN (Pix2PixHD) then translates these masks into realistic SEM images that preserve authentic textures and noise.

Key Findings

A segmentation model trained solely on this synthetic data achieves successful sim-to-real transfer to real SEM images.
It outperforms baselines trained on limited real datasets.
Because the underlying synthetic layouts are demonstrably 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.

Conclusion

The proposed pipeline offers a highly secure, high-performance solution for hardware assurance, addressing both data scarcity and design confidentiality simultaneously.

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*Source: arXiv:2508.03801*