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http://dx.doi.org/10.7736/KSPE.2015.32.5.463

A Study on Classification of Micro-Cracks in Silicon Wafer Through the Fusion of Principal Component Analysis and Neural Network  

Seo, Hyoung Jun (Aeronautical & Mechanical Design Engineering, Graduate School, Korea National University of Transportation)
Kim, Gyung Bum (Aeronautical & Mechanical Design Engineering, Korea National University of Transportation)
Publication Information
Journal of the Korean Society for Precision Engineering / v.32, no.5, 2015 , pp. 463-470 More about this Journal
Abstract
Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.
Keywords
Classification; Neural network; Micro-Crack; Silicon wafer; Principal component analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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