An Experimental Evaluation Of Fault Diagnosis From Imbalanced And Incomplete Data For Smart Semiconductor Manufacturing
Keywords
Classification; Data imputation; Fault detection; Machine learning; Semiconductor manufacturing
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.
Publication Date
12-1-2018
Publication Title
Big Data and Cognitive Computing
Volume
2
Issue
4
Number of Pages
1-20
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.3390/bdcc2040030
Copyright Status
Unknown
Socpus ID
85070301527 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85070301527
STARS Citation
Salem, Milad; Taheri, Shayan; and Yuan, Jiann Shiun, "An Experimental Evaluation Of Fault Diagnosis From Imbalanced And Incomplete Data For Smart Semiconductor Manufacturing" (2018). Scopus Export 2015-2019. 7333.
https://stars.library.ucf.edu/scopus2015/7333