FAULT DETECTION AND PREDICTION IN THE SEMICONDUCTOR MANUFACTURING PROCESS

Fault detection and prediction in the semiconductor industry will enable organizations to optimize their resources and maintain a high production yield for semiconductors. However, the data generated from the sensors are producing an abundance amount of data being kept in the repository and it is difficult for process engineers to analyze and make use of the dataset to make timely and accurate fault prediction and detection. Hence, this research will focus on developing a predictive model to predict and detect faulty equipment in the semiconductor manufacturing process. The quality of the data is enhanced through various analyses, feature selection and dimensionality reduction techniques. Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) will be the main algorithms used to develop the predictive models and their respective results compared to determine the best model to be used.