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

TL;DR

This paper presents a novel unsupervised clustering framework for semiconductor images that combines deep Topological Data Analysis, self-supervised learning, and transfer learning to effectively identify defect patterns in large-scale, unlabeled datasets.

Contribution

It introduces an integrated approach that leverages TDA, self-supervised, and transfer learning techniques for scalable, high-accuracy image clustering in semiconductor manufacturing.

Findings

Successfully identifies defect-related clusters in semiconductor images.

Reduces reliance on labeled data through self-supervised learning.

Enhances adaptability with transfer learning for new datasets.

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.