DIWA: device independent wafermap analysis

An automatic defect classification system for electrical test analysis of semiconductor wafer using a combination of a genetic algorithm as a feature selector and a RBF neural network as a classifier is introduced. An e-bitmap is obtained from the test stage and several features including mass, moments and invariant moments are extracted. These are presented to the feature selection method to generate an optimal set of features thus presented to the classifier. The performance of the reported approach show an 87% correct e-bitmap classification rate. The use of features gives this approach the capability to be device independent (i.e. independent of the format of die layout on a tested wafer).