Demystifying Defects: Federated Learning and Explainable AI for Semiconductor Fault Detection

Abstract

Semiconductor manufacturing, a critical driver of modern technology, involves intricate processes for fabricating integrated circuits on materials like silicon. This industry’s pivotal role spans various applications, from smartphones to computers, emphasizing the importance of fault detection to ensure the reliability and cost-efficiency of electronic devices. Fault detection within this sector entails collaboration among multiple stakeholders, including Original Equipment Manufacturers (OEMs), Integrated Device Manufacturers (IDMs), wafer foundries, and software providers. A common challenge is the reluctance to share sensitive design data centrally, which is essential for building traditional machine learning models. To overcome these challenges, this paper introduces an innovative fault detection model that leverages Federated Learning (FL) and Explainable AI (XAI). FL’s decentralized approach enhances model learning across multiple nodes without requiring the pooling of sensitive data, thus preserving data privacy. Concurrently, XAI ensures that the developed models maintain transparency and trustworthiness, even when trained on distributed datasets. This FL-based fault detection model permits stakeholders to train ML models on node-specific data without centralizing sensitive information. It accommodates heterogeneous and asynchronously-stored data, diverse machine learning models, and nodes with varying capacities and data volumes. By addressing the opacity of deep learning models, FL and XAI unveil their predictive behaviors in identifying semiconductor faults. Empirical results, obtained using a public dataset, demonstrate a significant improvement in defect identification precision, achieving an exceptional test accuracy of 98.78%. These findings underscore the potential of the proposed approach to transform fault detection in semiconductor manufacturing, thereby enhancing the reliability and efficiency of the production process.

Keywords

• Classification algorithms
• data privacy
• deep learning
• explainable artificial intelligence
• trusted AI
• semiconductor materials