Evolving neural networks using swarm intelligence for binmap classification

Emilio Miguelañez, A. M S Zalzala, Paul Tabor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A novel 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.

Original languageEnglish
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages978-985
Number of pages8
Volume1
Publication statusPublished - 2004
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States
Duration: 19 Jun 200423 Jun 2004

Conference

ConferenceProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
CountryUnited States
CityPortland, OR
Period19/06/0423/06/04

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