Defect recognition in line-space patterns aided by deep learning with data augmentation

Background: Finding optimal processing conditions to reduce defectivity is a major challenge in high-resolution lithographic tools such as directed self-assembly and extreme ultraviolet lithography. Aim: We aim to develop an efficient automated method that can detect defects and identify their types and locations, allowing for assessing the performance of lithographic processing conditions more easily. Approach: We propose a deep learning approach using an object recognition neural network for the classification and detection of defects in scanning electron microscopy (SEM) images featuring line-space patterns. We optimized our network by exploring its design variables and applied it on a case with limited numbers of SEM images available for training the network. Results: With an optimized network and data augmentation strategies (flipping and mixing images from simulations), it was poss…