用改进的Swin Transformer提升半导体划痕分割精度
ScratNet: A Swin-Based Multi-Scale Dilated Network with Precision Refinement for Semiconductor Scratch Segmentation
- 基于Swin Transformer设计多尺度空洞模块捕捉上下文
- 在10个数据集上平均Dice达92.3%，优于现有方法
- 适合高精度制造中细小不规则缺陷的自动检测
半导体制造中的表面划痕缺陷因形状不规则、对比度低和尺度多样而难以检测。传统方法在复杂成像条件下可靠性差，基于卷积神经网络的深度学习方法虽提升准确率，但常忽略细粒度边缘细节。为此，我们提出ScratNet，一种端到端划痕分割框架，采用改进的Swin Transformer主干网络与定制解码器。解码器包含多尺度空洞聚合（MDA）模块以捕捉局部与全局上下文，茎部融合模块（SIM）恢复空间细节，并引入精度优化（PR）分支，通过各向异性卷积增强边界锐度。通过阶段自适应特征聚合与边界感知优化，ScratNet在微小及不规则缺陷上表现更优。大量实验表明，该方法在10个数据集上平均Dice系数达92.3%，持续优于现有方法，为高精度制造中的自动化划痕检测提供了可扩展且鲁棒的解决方案。
原文摘要 · Abstract (English)
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.
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