Demystifying Defects
Federated Learning and Explainable AI for Semiconductor Fault Detection
The paper titled "Demystifying Defects: Federated Learning and Explainable AI for Semiconductor Fault Detection" introduces a novel approach to address the challenges in fault detection within the semiconductor manufacturing industry. Semiconductor manufacturing is a cornerstone of modern technology, crucial for producing devices ranging from smartphones to medical instruments. Detecting faults early in the manufacturing process is essential for ensuring product quality and reducing costs. Traditional fault detection methods often require the centralization of sensitive data, which can be problematic due to privacy concerns. The paper proposes the use of Federated Learning (FL) combined with Explainable AI (XAI) to create a decentralized model that respects data privacy while maintaining high detection accuracy. Federated Learning allows models to be trained on data distributed across multiple nodes without centralizing it, thereby preserving the confidentiality of proprietary designs and processes. Explainable AI, on the other hand, ensures that the model's decision-making process remains transparent and understandable to stakeholders.
In the proposed framework, each stakeholder, such as Original Equipment Manufacturers (OEMs) or Integrated Device Manufacturers (IDMs), retains control over their data and contributes to the training of a shared model. This collaborative approach leverages the strengths of diverse datasets and machine learning models across different nodes. The combination of FL and XAI not only enhances the predictive accuracy of the model but also provides insights into how the model identifies faults, which is critical for building trust in automated fault detection systems. Empirical results from testing the proposed model on a public dataset show a significant improvement in defect detection, achieving an impressive test accuracy of 98.78%. This underscores the potential of the proposed method to revolutionize fault detection in semiconductor manufacturing, making the production process more reliable and efficient while safeguarding sensitive data.