Pattern Recognition in Large-Scale Data Sets: Application in Integrated Circuit Manufacturing
| Autor: | Choudur Lakshminarayan, Michael Baron |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Mahalanobis distance
Markov random field Artificial neural network business.industry Computer science Pattern recognition Collinearity Missing data Linear discriminant analysis computer.software_genre Backpropagation ComputingMethodologies_PATTERNRECOGNITION Joint probability distribution Outlier Metric (mathematics) Artificial intelligence Data mining business computer |
| Zdroj: | Big Data Analytics ISBN: 9783319036885 BDA |
| Popis: | It is important in semiconductor manufacturing to identify probable root causes, given a signature. The signature is a vector of electrical test parameters measured on a wafer. Linear discriminant analysis and artificial neural networks are used to classify a signature of test electrical measurements of a failed chip to one of several pre-determined root cause categories. An optimal decision rule that assigns a new incoming signature of a chip to a particular root cause category is employed such that the probability of misclassification is minimized. The problem of classifying patterns with missing data, outliers, collinearity, and non-normality are also addressed. The selected similarity metric in linear discriminant analysis, and the network topology, used in neural networks, result in a small number of misclassifications. An alternative classification scheme is based on the locations of failed chips on a wafer and their spatial dependence. In this case, we model the joint distribution of chips by a Markov random field, estimate its canonical parameters and use them as inputs for the artificial neural network that also classifies the patterns by matching them to the probable root causes. |
| Databáze: | OpenAIRE |
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