Mixup-Based Neural Network for Image Restoration and Structure Prediction From SEM Images

Scanning electron microscopy (SEM) has been widely used for the semiconductor industry since it provides high-resolution (HR) details of the semiconductor. However, there is a gap in research for various tasks (i.e., image restoration (IR) and structure prediction) in SEM datasets collected under various conditions. Therefore, we introduce a new SEM dataset with diverse characteristics such as energy, noise, current with various levels for IR, and structure prediction. Furthermore, we propose a new deep-learning-based method for this dataset. The method consists of two stages: IR stage and structure prediction stage. In the IR stage, we design the transformer-based architecture to use pixel information widely. In the structure prediction stage, we introduce a novel training algorithm, SEMixup, and a novel CNN-based network, SEM structure prediction network (SEM-SPNet). Specifically, SEMixup overcomes the generalization and robustness of SEM-SPNet by implicitly interpolating a pair of samples and their labels. Experiments demonstrate that our method achieves state-of-the-art results across all dataset conditions. This work expands the possibilities of SEM image analysis using deep learning, contributing to the semiconductor industry.