Abstract
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.
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Lakshminarayan, C.K., Baron, M.I. (2013). Pattern Recognition in Large-Scale Data Sets: Application in Integrated Circuit Manufacturing. In: Bhatnagar, V., Srinivasa, S. (eds) Big Data Analytics. BDA 2013. Lecture Notes in Computer Science, vol 8302. Springer, Cham. https://doi.org/10.1007/978-3-319-03689-2_13
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DOI: https://doi.org/10.1007/978-3-319-03689-2_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03688-5
Online ISBN: 978-3-319-03689-2
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