Associate Professor National University of Singapore
Disclosure: Disclosure information not submitted.
In semiconductor manufacturing, wafer testing is performed to ensure the performance of each product after wafer fabrication. The wafer map is used to visualize the color-coded wafer test results based on the locations. The defects on the wafer map may be randomly distributed or form clustered patterns. The various clustered defect patterns are usually caused by assignable faults. The identification of the patterns is thus important to provide valuable hints for the root causes diagnosis. Solving the problems helps improve the manufacturing processes and reduce costs. In this study, we propose a new wafer map defect pattern recognition method to solve the above problems. Motivated by the success of convolutional neural network (CNN), we present a framework for the task of wafer map defect pattern recognition. Our method uses polar mapping before the training of CNN to transform the circular wafer map into a matrix which can be processed within CNN architecture. This procedure also reduces the input size and solves variation in wafer and die sizes. To eliminate the effects of rotation, we apply data augmentation in the training of CNN. Experiments using real-world dataset prove the effectiveness and superiority of our method.