Abstract
We introduce a probabilistic extension of non-negative matrix factorization (NMF) by considering binary coded images as a probabilistic superposition of underlying continuous-valued elementary patterns. We provide an appropriate algorithm to solve the related optimization problem with non-negativity constraints which represents an extension of the well-known NMF-algorithm to binary-valued data sets. We demonstrate the performance of our method by applying it to the detection and characterization of hidden causes for failures during semi-conductor wafer processing. We decompose binary coded (pass/fail) wafer test data into underlying elementary failure patterns and study their influence on the performance of single wafers during testing.
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Schachtner, R., Pöppel, G., Lang, E.W. (2009). Binary Nonnegative Matrix Factorization Applied to Semi-conductor Wafer Test Sets. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_89
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DOI: https://doi.org/10.1007/978-3-642-00599-2_89
Publisher Name: Springer, Berlin, Heidelberg
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