TY - GEN
T1 - Pattern recognition in large-scale data sets
T2 - 2nd International Conference on Big Data Analytics, BDA 2013
AU - Lakshminarayan, Choudur K.
AU - Baron, Michael I.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Back Propagation
KW - Failure Analysis
KW - Integrated-Circuit
KW - Linear Discriminant Analysis
KW - Mahalanobis Distance
KW - Markov Random Fields and spatial signatures
KW - Neural Networks
KW - Pattern Recognition
KW - Signature Analysis
UR - http://www.scopus.com/inward/record.url?scp=84893366275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893366275&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-03689-2_13
DO - 10.1007/978-3-319-03689-2_13
M3 - Conference contribution
AN - SCOPUS:84893366275
SN - 9783319036885
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 196
BT - Big Data Analytics - Second International Conference, BDA 2013, Proceedings
Y2 - 16 December 2013 through 18 December 2013
ER -