Synthetic Data Pipeline for Privacy-Preserving Hardware Assurance via SEM Image Generation

Forum topic · 小凯 · 2026-08-11

Summary

Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but building large datasets for automated analysis is hampered by time-intensive acquisition and strict intellectual property (IP) constraints on proprietary designs. This paper proposes a privacy-preserving pipeline that heavily distorts functional designs to protect IP while generating visually realistic synthetic SEM datasets from only a small set of initial examples. A StyleGAN learns the distribution of hardware layout masks to produce novel, macroscopically diverse structures, after which a conditional GAN (Pix2PixHD) translates these masks into realistic SEM images that preserve authentic textures and noise. Trained exclusively on synthetic data, the segmentation model not only achieves successful sim-to-real transfer to real images but also outperforms baselines trained on limited real datasets. Because the synthetic layouts are novel and do not replicate specific proprietary routing, deploying the final model reduces exposure to gradient inversion and membership inference attacks, offering a secure, high-performance solution for hardware assurance.

Overview

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

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 model trained solely on such synthetic data not only demonstrates successful sim-to-real transfer to real images but also outperforms baseline models trained on limited real datasets. 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, providing a highly secure, high-performance solution for hardware assurance.

Key Points

Problem: Hardware assurance via SEM image analysis is bottlenecked by slow data collection and strict IP restrictions on proprietary chip layouts.
Two-stage generative pipeline:
*Stage 1 — StyleGAN*: Learns the distribution of hardware layout masks and produces novel, macroscopically diverse structures that intentionally distort the original designs to protect IP.
*Stage 2 — Pix2PixHD (conditional GAN)*: Translates the synthetic masks into realistic SEM-like images that retain authentic textures and noise characteristics.
Sim-to-real transfer: A segmentation model trained entirely on synthetic data generalizes successfully to real SEM images.
Performance gain: The synthetic-only segmentation model outperforms baselines trained on the limited available real dataset.
Privacy guarantee: Because the generated layouts are structurally novel and do not replicate proprietary routing, downstream models are less vulnerable to gradient inversion and membership inference attacks that aim to extract sensitive IP.
Practical impact: Provides a scalable, secure data path for deploying automated SEM-based hardware verification while reducing dependence on scarce, IP-sensitive real datasets.

Source

Auto-collected 2026-08-12

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