🤖 AI Summary
This work addresses the challenge of reliably detecting surface scratches in semiconductor manufacturing, which are difficult to identify due to their irregular shapes, low contrast, and varying scales. To this end, the authors propose ScratNet, an end-to-end scratch segmentation framework built upon an enhanced Swin Transformer backbone and a custom-designed decoder. ScratNet introduces several key innovations: a multi-scale dilated aggregation module, a stem integration mechanism, and an anisotropic convolution-driven boundary refinement branch, collectively enabling stage-adaptive feature fusion and boundary-aware optimization. Experimental results demonstrate that ScratNet significantly outperforms existing methods under diverse and complex imaging conditions, achieving superior detection accuracy and robustness—particularly for fine and irregular scratches.
📝 Abstract
Surface scratch defects in semiconductor manufacturing pose significant challenges due to their irregular shapes, low contrast, and varying scales. Traditional inspection methods often struggle to detect such defects reliably, especially in complex imaging scenarios. While deep learning approaches based on Convolutional Neural Networks (CNNs) have improved accuracy, they often fail to capture fine-grained edge details. To address these limitations, we propose ScratNet, a novel end-to-end scratch segmentation framework that integrates a modified Swin Transformer backbone with a tailored decoder. The decoder incorporates a Multi-Scale Dilated Aggregation (MDA) module to capture both local and global context, a Stem Integration Module (SIM) to restore spatial detail, and a Precision Refinement (PR) branch that enhances boundary sharpness using anisotropic convolutions. Through this stage-adaptive feature aggregation and boundary-aware refinement, ScratNet achieves superior accuracy on thin and irregular defects. Extensive experiments demonstrate that ScratNet consistently outperforms existing methods, providing a scalable and robust solution for automated scratch inspection in high-precision manufacturing.