Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques

2025/05/05 by Janhavi Giri, Giri, Janhavi, Attila Lengyel +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Image Processing Techniques #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topological and Geometric Data Analysis

paper · pdf · doi:10.48550/arxiv.2505.03848

openalex publication_date 2025/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces
• Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
• Iterative Cluster Harvesting for Wafer Map Defect Patterns
• Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation
• Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
• A novel approach for wafer defect pattern classification based on topological data analysis

Related

• DECOR: Deep Embedding Clustering with Orientation Robustness
• Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing
• SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection
• An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection
• A novel approach for wafer defect pattern classification based on topological data analysis