ArXiv

A DDPM-based pipeline was trained from scratch with only 15 TEM samples using a…

2026-06-23 · 17:02 UTC ·Johannes Boehm, Bappaditya Dey ·1 min read
Authors
Johannes Boehm, Bappaditya Dey
Categories
cs.CV, eess.IV
arXiv
https://arxiv.org/abs/2606.24817v1
PDF
https://arxiv.org/pdf/2606.24817v1

Brief

The paper presents a DDPM framework to generate high-fidelity synthetic TEM images for data-limited semiconductor metrology, training from scratch on only 15 samples via a progressive patch-based schedule. It combines TrivialAugment adaptation, cross-process transfer, classifier guidance and RePaint-style inpainting to preserve TEM noise and global structure, achieves MS-SSIM > 0.98, and repurposes encoder features for segmentation. Summary based on the abstract.

Why it matters

A DDPM-based pipeline was trained from scratch with only 15 TEM samples using a progressive patch-based strategy to scale from low-resolution patches to full images, producing synthetic TEM images with reported MS-SSIM > 0.98.

Key details

  • The approach integrates TrivialAugment adaptation, cross-process domain transfer, classifier guidance, and RePaint-style inpainting to capture TEM-specific noise, structural detail and global spatial relationships; DDPM encoder feature maps are partitioned to yield coherent segmentation masks.
  • Outputs were qualitatively assessed by experts and claimed to support downstream ML for defect detection, segmentation and FAB metrology; paper by Johannes Boehm and Bappaditya Dey (arXiv 2026-06-23) to be presented at the 2026 International Symposium ELMAR (IEEE).
Source evidence

Abstract

Advanced semiconductor nodes drastically increased demand for Transmission Electron Microscopy (TEM), yet destructive sample preparation, slow imaging and high costs severely limit the availability of diverse datasets needed for downstream machine learning (ML). Synthetic data generation is becoming essential, but current generative models often miss TEM-specific noise, structural detail, and stochastic variability crucial for evaluation. We present a Denoising Diffusion Probabilistic Model (DDPM) framework for synthetic TEM image generation under extreme data scarcity. A progressive patch-based training strategy scales from low-resolution patches to full images, enabling from-scratch training with only 15 samples. We integrate a custom TrivialAugment adaptation, cross-process domain transfer, classifier guidance, and RePaint-style inpainting, culminating in full-image generation that preserves global structural and spatial relationships in compliance with FAB metrology requirements. Beyond synthesis, we repurpose DDPM feature representations for segmentation, partitioning encoder feature maps to obtain coherent region masks. Our synthetic images achieve up to MS-SSIM > 0.98 and qualitative expert assessment consistent with structural similarity results, facilitating downstream ML training for defect detection, segmentation, and metrology while preserving statistical and physical realism.

Comment: To be presented at the 2026 International Symposium ELMAR, published by IEEE in the conference proceedings