Integrated Machine Learning Models for Defect Analysis in Semiconductor Industry

In the semiconductor industry, effective defect management is crucial to maintain high standards of quality and reliability. Components acquired from various suppliers with differing specifications can introduce defects that, if not promptly addressed, may spread throughout the supply chain. Traditional defect analysis methods, often reliant on trial and error, are inadequate for handling the complexity and volume of defects in modern manufacturing settings. This research presents an integrated approach that combines machine learning techniques to enhance defect analysis in semiconductor industry. A case study from a semiconductor manufacturing company known for its rigorous product testing processes to ensure product quality and reliability is considered. Our approach aids decision-makers in analyzing defects and devising proactive strategies to prevent them. Algorithms such as decision trees, random forests, and neural networks are applied to an extensive dataset containing over 5,000 defect instances from multiple structured and unstructured databases with varied features. The models identify root causes and predict the best resolutions for detected defects. Results from the case study demonstrate that machine learning significantly improves the accuracy and efficiency of root cause identification and resolution prediction.