A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing

A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.