Region Segmentation for Efficient Semiconductor Inspection: A Deep Learning Approach with Transformers and Atrous Convolution

This paper explores the application of deep learning to automate the traditionally manual creation of inspection recipes for machine vision scenarios requiring complex region selection, such as those found in semiconductor manufacturing. Manually selecting and cropping functional regions in ultra-high-resolution images for analysis and inspection can take anywhere from tens of minutes to hours. To address this challenge, we propose a model whose encoder integrates atrous convolution into a transformer architecture for better feature extraction. This approach is designed to improve segmentation accuracy while maintaining efficiency in processing large-scale semiconductor images. By automating the selection and cropping process, the proposed method aims to streamline quality inspection workflows, reduce manual labor, and accelerate automated optical inspection. Experimental results demonstrate that the model achieves high segmentation performance, with segmentation accuracy reaching 98% and a faster model inference, making it a practical and effective solution for enabling large-scale automation in semiconductor inspection. This research highlights the potential of deep learning-based methods to transform inspection processes, ensuring higher efficiency and product quality across semiconductor manufacturing industries.