Defect simulation in SEM images using generative adversarial networks

SEM image processing is an important part of semiconductor manufacturing. However, one difficulty of SEM image processing is collecting enough defect-containing samples of defect-of-interests (DOI) because many DOIs are very rare. This problem becomes more prominent for Machine Learning (ML) or Deep Learning (DL) based image processing techniques since they require large amount of samples for training. In this paper, we present a Generative Adversarial Networks (GAN) based defect simulation framework to tackle this problem. The fundamental insight of our approach is that we treat the defect simulation problem as an image style transfer problem. Following this thought, we train a neural network model to turn a defect-free image into a defect- containing image. We evaluate the proposed defect simulation framework by using it as a data augmentation method for ML/DL based Automatic Defect Classification (ADC) and Image Quality Enhancement (IQE) on a Line Pattern Dataset, which is collected with ASML ePTMand eScan R series inspection tools from an ASML standard wafer. The experimental results show a significant performance gain for both ADC and IQE. The result proves our defect simulation framework is effective. We expect GAN based defect simulation can have a broader impact in many other SEM image development and engineering applications in the future.