Semantic Segmentation for Noisy and Limited Wafer Transmission Electron Microscope Images

Semantic segmentation for automated measurement in semiconductor manufacturing, specifically with wafer transmission electron microscopy (TEM) images, poses significant challenges because of the difficulty of acquisition, prevalent noise, and ambiguous object boundaries. However, prior studies focused on broadening the application of semantic segmentation for automated measurement without considering the specific intricacies of TEM images. In this study, we propose a wafer TEM images-specific semantic segmentation and transfer learning (WTEM-SST) framework to address these issues. The proposed WTEM-SST involves a pre-training stage, wafer TEM-specific data augmentation methods, and a boundary-focused loss function. The pre-training stage addresses the difficulty of collecting and annotating wafer TEM images, followed by fine-tuning for process-specific segmentation models. Our data augmentation techniques mitigate challenges related to limited training samples, lots of noise, and unclear boundaries. The boundary-focused loss makes the model more precise in boundary recognition during fine-tuning. We demonstrate that WTEM-SST outperforms conventional segmentation models, with our studies highlighting the effectiveness of the three components in WTEM-SST.