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Authors: Du-Ming Tsai 1 ; Morris Fan 2 ; Yi-Quan Huang 1 and Wei-Yao Chiu 1

Affiliations: 1 Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Road, Chung-Li, Taiwan and Republic of China ; 2 Department of Industrial Engineering and Management, National Taipei University of Technology, 1 Sec. 3 Zhongxiao E. Rd., Taipei, Taiwan and Republic of China

ISBN: 978-989-758-354-4

Keyword(s): Defect Detection, Multicrystalline Solar Wafer, Saw Mark, Deep Learning.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis

Abstract: This paper presents a machine vision-based scheme to automatically detect saw-mark defects in solar wafer surfaces. A saw-mark defect is a severe flaw when cutting a silicon ingot into wafers. A multicrystalline solar wafer surface presents random shapes, sizes and orientations of crystal grains in the surface and, thus, results in a heterogeneous texture. It makes the automatic visual inspection task extremely difficult. The deep learning technique is an ideal choice to tackle the problem, but it requires a huge amount of positive (defect-free) and negative (defective) samples for the training. The negative samples are generally not sufficient enough in a manufacturing process. We thus apply a GAN-based model to generate the defective samples for training, and then use the true defect-free samples and the synthesized defective samples to train a CNN model. It solves the imbalanced data arising in manufacturing inspection. The preliminary experiment has shown promising results of the proposed method for detecting various saw-mark defects including black line, white line, and impurity in multicrystalline solar wafers. (More)

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Paper citation in several formats:
Tsai, D.; Fan, M.; Huang, Y. and Chiu, W. (2019). Saw-Mark Defect Detection in Heterogeneous Solar Wafer Images using GAN-based Training Samples Generation and CNN Classification.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 234-240. DOI: 10.5220/0007306602340240

@conference{visapp19,
author={Du{-}Ming Tsai. and Morris S. K. Fan. and Yi{-}Quan Huang. and Wei{-}Yao Chiu.},
title={Saw-Mark Defect Detection in Heterogeneous Solar Wafer Images using GAN-based Training Samples Generation and CNN Classification},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2019},
pages={234-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007306602340240},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - Saw-Mark Defect Detection in Heterogeneous Solar Wafer Images using GAN-based Training Samples Generation and CNN Classification
SN - 978-989-758-354-4
AU - Tsai, D.
AU - Fan, M.
AU - Huang, Y.
AU - Chiu, W.
PY - 2019
SP - 234
EP - 240
DO - 10.5220/0007306602340240

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