Title: High-Fidelity Synthetic Transmission Electron Microscopy Image Generation Using Diffusion Probabilistic Models for Data-Limited Semiconductor Metrology
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Denoising Diffusion Probabilistic Model (DDPM), Semiconductor Manufacturing, Transmission Electron Microscopy (TEM), Generative AI, Synthetic Data Generation, High-Resolution Imaging, Semiconductor Metrology, Defect Inspection, Extreme Ultraviolet Lithography (EUVL), Data Augmentation

Abstract: Advanced semiconductor nodes have significantly increased the demand for Transmission Electron Microscopy (TEM) characterization, posing unprecedented challenges for metrology extraction and defect inspection due to device complexity and shrinking critical-dimension (CD). However, the destructive nature of TEM sample preparation, combined with time-intensive imaging, high acquisition costs, and reproducibility issues, severely limits the availability of diverse datasets required for conventional experimental analysis and, consequently, for training machine learning (ML) models. As a result, artificial intelligence-based synthetic data generation and augmentation have become essential in semiconductor TEM research. Existing generative approaches often fail to capture the complex noise characteristics, surface features, and stochastic variations present in real TEM images, which are critical for accurate semiconductor metrology evaluation. In this research, we present a novel generative framework utilizing Denoising Diffusion Probabilistic Models (DDPMs) specifically designed for synthetic TEM image generation under extreme data scarcity. Our approach employs a progressive patch-based training strategy that scales from low-resolution patches to full-resolution images, enabling from-scratch model training with datasets containing as few as 15 images. We integrate a custom adaptation of the TrivialAugment (TA) algorithm, incorporate domain transfer for cross-process compatibility, and apply super-resolution-enabling inpainting techniques alongside classifier guidance. This framework culminates in full-image training, generating coherent high-resolution TEM images that preserve global structural and spatial relationships essential for analyzing large-scale device structures, in compliance with real FAB semiconductor metrology requirements. Our synthetic images are visually indistinguishable from real TEM data, with a high structural similarity index (MS-SSIM > 0.94) confirming their high-fidelity reproduction, as validated by domain experts. The generated synthetic datasets aim to facilitate robust training of downstream ML models for defect detection, grain and phase boundary segmentation, and metrology applications, addressing critical data availability bottlenecks in advanced semiconductor manufacturing while preserving the statistical and physical properties of scarce real TEM imaging data.

TL;DR: We propose a progressive diffusion-based generative framework that aims to generate high-fidelity synthetic TEM images from limited data, enabling reliable semiconductor metrology and ML training despite costly, destructive real TEM acquisition.