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Big Data Cogn. Comput. 2018, 2(4), 30; https://doi.org/10.3390/bdcc2040030

An Experimental Evaluation of Fault Diagnosis from Imbalanced and Incomplete Data for Smart Semiconductor Manufacturing

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
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Received: 18 July 2018 / Revised: 10 September 2018 / Accepted: 14 September 2018 / Published: 21 September 2018
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Abstract

The SECOM dataset contains information about a semiconductor production line, entailing the products that failed the in-house test line and their attributes. This dataset, similar to most semiconductor manufacturing data, contains missing values, imbalanced classes, and noisy features. In this work, the challenges of this dataset are met and many different approaches for classification are evaluated to perform fault diagnosis. We present an experimental evaluation that examines 288 combinations of different approaches involving data pruning, data imputation, feature selection, and classification methods, to find the suitable approaches for this task. Furthermore, a novel data imputation approach, namely “In-painting KNN-Imputation” is introduced and is shown to outperform the common data imputation technique. The results show the capability of each classifier, feature selection method, data generation method, and data imputation technique, with a full analysis of their respective parameter optimizations. View Full-Text
Keywords: classification; data imputation; fault detection; machine learning; semiconductor manufacturing classification; data imputation; fault detection; machine learning; semiconductor manufacturing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Salem, M.; Taheri, S.; Yuan, J.-S. An Experimental Evaluation of Fault Diagnosis from Imbalanced and Incomplete Data for Smart Semiconductor Manufacturing. Big Data Cogn. Comput. 2018, 2, 30.

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