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

arXiv: https://arxiv.org/abs/2505.03848 · HTML: https://mlsanity.com/paper/2505.03848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 7 of 7)

- [A novel approach for wafer defect pattern classification based on topological data analysis](https://mlsanity.com/paper/2209.08945/) (2022)
- [Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation](https://mlsanity.com/paper/2311.12840/) (2023)
- [Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation](https://mlsanity.com/paper/2306.00202/) (2023)
- [Iterative Cluster Harvesting for Wafer Map Defect Patterns](https://mlsanity.com/paper/2404.15436/) (2024)
- [Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network](https://mlsanity.com/paper/2411.11029/) (2024)
- [Topological Data Analysis in smart manufacturing: State of the art and futuredirections](https://mlsanity.com/paper/2310.09319/) (2023)
- [Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces](https://mlsanity.com/paper/2411.11421/) (2024)

## Abstract (arXiv)

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.