RLIF-Net:Unsupervised Trace-SPC Fault Detection Solution Based on Representation Learning and Isolation Forest

Wafer manufacturing is a complex process involving hundreds of process steps. Detecting and identifying potential anomalies and malfunctions in sensor parameters are crucial for improving production yield. However, traditional rule-based or statistical methods fail to meet the requirements of accuracy and efficiency. To address this issue, we propose an innovative model that combines deep learning with Isolation Forest. The deep learning module is used to extract multi-dimensional feature vectors at each timestamp, while the Isolation Forest module takes the multi-dimensional feature vectors at each timestamp as input for anomaly detection in the timestamp dimension. We conducted experiments using a real industrial dataset and compared our model with several state-of-the-art models. The results demonstrate that our model exhibits strong learning and representation capabilities, enabling it to learn from large amounts of data and identify complex anomaly patterns.