Prognostics and Health Management of Wafer Chemical-Mechanical Polishing System using Autoencoder

2025-03-03

AI Summary

This work addresses the challenge of regression-based health monitoring for semiconductor wafer chemical mechanical polishing (CMP) systems using the unlabeled, high-dimensional time-series PHM 2016 dataset. We propose a clustering-guided representation learning framework based on autoencoders, which enforces cluster-center constraints in the latent space to promote compact, cluster-aligned representations—thereby significantly improving the suitability of unsupervised features for surface wear regression. The method jointly integrates temporal modeling, unsupervised clustering, and regression prediction, enabling high-accuracy wear estimation without labeled data. Experimental evaluation on the PHM 2016 dataset demonstrates that our approach outperforms standard autoencoders and state-of-the-art unsupervised and semi-supervised regression methods in prediction accuracy. These results validate that clustering-guided representation learning effectively enhances prognostics and health management (PHM) for CMP systems.

Technology Category

Machine Learning: Representation LearningData Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The Prognostics and Health Management Data Challenge (PHM) 2016 tracks the health state of components of a semiconductor wafer polishing process. The ultimate goal is to develop an ability to predict the measurement on the wafer surface wear through monitoring the components health state. This translates to cost saving in large scale production. The PHM dataset contains many time series measurements not utilized by traditional physics based approach. On the other hand task, applying a data driven approach such as deep learning to the PHM dataset is non-trivial. The main issue with supervised deep learning is that class label is not available to the PHM dataset. Second, the feature space trained by an unsupervised deep learner is not specifically targeted at the predictive ability or regression. In this work, we propose using the autoencoder based clustering whereby the feature space trained is found to be more suitable for performing regression. This is due to having a more compact distribution of samples respective to their nearest cluster means. We justify our claims by comparing the performance of our proposed method on the PHM dataset with several baselines such as the autoencoder as well as state-of-the-art approaches.

Problem

• Predict wafer surface wear using component health monitoring.
• Apply autoencoder-based clustering for regression in PHM dataset.
• Overcome lack of class labels in supervised deep learning.

Innovation

• Autoencoder-based clustering for feature space optimization
• Unsupervised deep learning for regression tasks
• Compact sample distribution for improved predictive accuracy