Silicon Wafer Production Monitoring

Silicon wafer production monitoring aims to improve yield and quality by detecting defects and predicting process outcomes using sensor data and wafer defect maps. Current research heavily utilizes machine learning, employing algorithms like LSTM networks, gradient boosting trees, and specialized convolutional neural networks designed for sparse data, to analyze sensor readings and image data for defect detection and process prediction. These advanced analytical techniques offer significant potential for optimizing semiconductor manufacturing processes, leading to increased efficiency and reduced costs.