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A Study on Performance Evaluation of Typical Classification Techniques for Micro-cracks of Silicon Wafer  

Kim, Sang Yeon (Graduate school, Korea National University of Transportation)
Kim, Gyung Bum (Aeronautical & Mechanical Design Engineering, Korea National University of Transportation)
Publication Information
Journal of the Semiconductor & Display Technology / v.15, no.3, 2016 , pp. 6-11 More about this Journal
Abstract
Silicon wafer is one of main materials in solar cell. Micro-cracks in silicon wafer are one of reasons to decrease efficiency of energy transformation. They couldn't be observed by human eye. Also, their shape is not only various but also complicated. Accordingly, their shape classification is absolutely needed for manufacturing process quality and its feedback. The performance of typical classification techniques which is principal component analysis(PCA), neural network, fusion model to integrate PCA with neural network, and support vector machine(SVM), are evaluated using pattern features of micro-cracks. As a result, it has been confirmed that the SVM gives good results in micro-crack classification.
Keywords
Classification technique; Micro-crack; Silicon wafer;
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Times Cited By KSCI : 1  (Citation Analysis)
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