An automatic defect classification system is introduced for electrical test analysis of semiconductor wafer using evolutionary algorithm techniques to construct radial basis function neural networks (RBF NNs) as a classifier. The parameters of a RBF NN (number of neurons, and their respective centers and radii) are often determined by hand or based on methods highly dependent on initial values. In this work, particle swarm optimization algorithm is implemented to build a RBF NN that solves this specific problem. As a primary input source to the network, the system employs electrical binmaps obtained from the test stage of the manufacturing process. To accomplish this task, a filtering algorithm is also implemented able to discard those wafermaps without pattern. The performance of the reported approach shows an outstanding e-bitmap classification rate. To evaluate the performance of the main algorithm, the system is tested also on the Australian credit card data set and the error rate obtained is comparable with the best algorithms found in the literature.
['Emilio Miguelanez', 'A.M.S. Zalzala', 'Paul Tabor']
Evolving neural networks using swarm intelligence for binmap classification