TY - GEN
T1 - Evolving an automatic defect classification tool
AU - Glazer, Assaf
AU - Sipper, Moshe
PY - 2008/7/21
Y1 - 2008/7/21
N2 - Automatic Defect Classification (ADC) is a well-developed technology for inspection and measurement of defects on patterned wafers in the semiconductors industry. The poor training data and its high dimensionality in the feature space render the defect-classification task hard to solve. In addition, the continuously changing environment-comprising both new and obsolescent defect types encountered during an imaging machine's lifetime-require constant human intervention, limiting the technology's effectiveness. In this paper we design an evolutionary classification tool, based on genetic algorithms (GAs), to replace the manual bottleneck and the limited human optimization capabilities. We show that our GA-based models attain significantly better classification performance, coupled with lower complexity, with respect to the human-based model and a heavy random search model.
AB - Automatic Defect Classification (ADC) is a well-developed technology for inspection and measurement of defects on patterned wafers in the semiconductors industry. The poor training data and its high dimensionality in the feature space render the defect-classification task hard to solve. In addition, the continuously changing environment-comprising both new and obsolescent defect types encountered during an imaging machine's lifetime-require constant human intervention, limiting the technology's effectiveness. In this paper we design an evolutionary classification tool, based on genetic algorithms (GAs), to replace the manual bottleneck and the limited human optimization capabilities. We show that our GA-based models attain significantly better classification performance, coupled with lower complexity, with respect to the human-based model and a heavy random search model.
UR - https://www.scopus.com/pages/publications/47249130829
U2 - 10.1007/978-3-540-78761-7_20
DO - 10.1007/978-3-540-78761-7_20
M3 - Conference contribution
AN - SCOPUS:47249130829
SN - 3540787607
SN - 9783540787600
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 194
EP - 203
BT - Applications of Evolutionary Computing - EvoWorkshops 2008
T2 - European Workshops on the Theory and Applications of Evolutionary Computation, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog
Y2 - 26 March 2008 through 28 March 2008
ER -