Automated Wafer Defect Classification using a Convolutional Neural Network Augmented with Distributed Computing

Abstract

This research compares the traditional machine learning algorithms and deep learning technology. We report our distributed computing convolutional neural network deep learning platform design and results in wafer defect classification. The result shows that the classification accuracy and purity performance is better than that of traditional machine learning models like Random Forest.