融合拓扑分析与自监督学习，实现半导体图像无监督聚类。
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques
- 用深度拓扑数据分析图像内在结构，捕捉细微模式。
- 在无标签数据上训练，降低对标注数据依赖。
- 支持迁移学习，快速适配新数据集，适合工业质检场景。
半导体制造产生海量图像数据，对缺陷识别和良率优化至关重要，但常超出人工检测能力。传统聚类方法难以处理高维无标签数据，限制了对复杂模式的捕捉。本文提出一种先进聚类框架，整合深度拓扑数据科学（TDA）、自监督学习与迁移学习技术，实现无监督图像聚类。TDA挖掘数据内在拓扑特征，自监督学习从无标签数据中提取有意义表示，减少对标注数据的依赖；迁移学习提升框架的可扩展性，支持在不从头训练的前提下微调至新数据集。在合成及开源半导体图像数据集上验证，该框架成功识别出与缺陷模式和工艺变化一致的聚类。研究展示了将TDA、自监督学习与迁移学习结合的变革潜力，为半导体制造及其他大规模图像数据场景提供可扩展的主动过程监控与质量控制解决方案。
原文摘要 · Abstract (English)
Semiconductor manufacturing generates vast amounts of image data, crucial for defect identification and yield optimization, yet often exceeds manual inspection capabilities. Traditional clustering techniques struggle with high-dimensional, unlabeled data, limiting their effectiveness in capturing nuanced patterns. This paper introduces an advanced clustering framework that integrates deep Topological Data Analysis (TDA) with self-supervised and transfer learning techniques, offering a novel approach to unsupervised image clustering. TDA captures intrinsic topological features, while self-supervised learning extracts meaningful representations from unlabeled data, reducing reliance on labeled datasets. Transfer learning enhances the framework's adaptability and scalability, allowing fine-tuning to new datasets without retraining from scratch. Validated on synthetic and open-source semiconductor image datasets, the framework successfully identifies clusters aligned with defect patterns and process variations. This study highlights the transformative potential of combining TDA, self-supervised learning, and transfer learning, providing a scalable solution for proactive process monitoring and quality control in semiconductor manufacturing and other domains with large-scale image datasets.
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