用少量标注数据精准识别芯片缺陷，提升半导体检测效率。
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image
- 基于CLIP模型定制化改进，专注缺陷区域，抑制背景干扰。
- 仅需少量标注数据即可实现高精度分类与分割，准确率显著提升。
- 融合领域知识文本提示，适合半导体缺陷检测场景快速部署。
在集成电路制造领域，纳米级晶圆缺陷的检测与分类对后续根本原因分析和良率提升至关重要。扫描电子显微镜（SEM）图像中复杂的背景图案及缺陷多样的纹理带来了巨大挑战。传统方法常因数据、标签不足且迁移能力差而受限。本文提出一种新型少样本学习方法SEM-CLIP，用于精确的缺陷分类与分割。该方法定制对比语言-图像预训练（CLIP）模型，更聚焦缺陷区域并减少背景干扰，从而提升分割精度。我们采用包含领域知识的文本提示作为先验信息，辅助精确分析；同时结合文本引导的特征工程，更有效地对缺陷进行分类。SEM-CLIP仅需极少标注数据，大幅降低半导体行业的标注人力成本。大量实验证明，该模型在少样本学习场景下实现了出色的分类与分割性能。
原文摘要 · Abstract (English)
In the field of integrated circuit manufacturing, the detection and classification of nanoscale wafer defects are critical for subsequent root cause analysis and yield enhancement. The complex background patterns observed in scanning electron microscope (SEM) images and the diverse textures of the defects pose significant challenges. Traditional methods usually suffer from insufficient data, labels, and poor transferability. In this paper, we propose a novel few-shot learning approach, SEM-CLIP, for accurate defect classification and segmentation. SEM-CLIP customizes the Contrastive Language-Image Pretraining (CLIP) model to better focus on defect areas and minimize background distractions, thereby enhancing segmentation accuracy. We employ text prompts enriched with domain knowledge as prior information to assist in precise analysis. Additionally, our approach incorporates feature engineering with textual guidance to categorize defects more effectively. SEM-CLIP requires little annotated data, substantially reducing labor demands in the semiconductor industry. Extensive experimental validation demonstrates that our model achieves impressive classification and segmentation results under few-shot learning scenarios.
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