Semiconductor defect classification using hyperellipsoid clustering neural networks and model switching

An automatic defect classification (ADC) system for visual inspection of semiconductor wafers, using a neural network classifier is introduced. The proposed Hyperellipsoid Clustering Network (HCN) employing a Radial Basis Function (RBF) in the hidden layer, is trained with additional penalty conditions for recognizing unfamiliar inputs as originating from an unknown defect class. Also, by using a dynamic model alteration method called Model Switching, a reduced-model classifier which enables an efficient classification is obtained. In the experiments, the effectiveness of the unfamiliar input recognition was confirmed, and a classification rate sufficiently high for use in the semiconductor fab was obtained.