Evaluating the Performance of Different Classification Algorithms for Fabricated Semiconductor Wafers

Abstract

Defect detection and classification is crucial in ensuring product quality and reliability. Classification provides information on problems related to the detected defects which can then be used to perform yield prediction, fault diagnosis, correcting manufacturing issues and process control. Accurate classification requires good selection of features to help distinguish between different cluster types. This research investigates the use of two features for classification: Polar Fourier Transform (PFT) and image Rotational Moment Invariant (RMI). It provides a comprehensive critical evaluation of several classification schemes in terms of performance and accuracy based on these features. It concludes by discussing the suitability of each classifier for classifying different types of defect clusters on fabricated semiconductor wafers.