Polycrystalline Silicon Wafer Surface Color Defect Inspection Based on Machine Vision

Han Wang; Rui Miao; Xiao Song Wu; Wang Yuan Ni; Xu Chen Tang

doi:10.4028/www.scientific.net/AMM.548-549.48

Paper Titles

Rigidity Analysis of Kenaf Thermoplastic Composites Using Halpin-Tsai Equation
p.29

Preparation and Characteristics of Electrospun Silver-Containing PHBV Ultrafine Fiber
p.34

Pore Size Control of Ordered Mesoporous Carbons and Adsorption Performance of Dye Molecules
p.38

Investigating the Effects of Blending Ratio and Injection Parameters on the Tensile Properties of Glass Fiber-Filled Nylon 66 Composite Gear
p.43

Polycrystalline Silicon Wafer Surface Color Defect Inspection Based on Machine Vision
p.48

Investigation into the Differences Thermal Properties of SLS Polymer Powder Materials Using Differential Scanning Calorimetry (DSC)
p.53

Preparation of High Solid Content Polyurethane Emulsion with Sulfonate Hydrophilic Chain-Extending Agents
p.57

Asbestos and Alternative Materials
p.61

Relationship between Foamability and Nanoparticle Concentration of Carbon Dioxide (CO₂) Foam for Enhanced Oil Recovery (EOR)
p.67

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 548-549Polycrystalline Silicon Wafer Surface Color Defect...

Polycrystalline Silicon Wafer Surface Color Defect Inspection Based on Machine Vision

Abstract:

As non-uniform color and complex texture exist on the polycrystalline silicon solar cell, manual surface inspection adopted by most domestic factories suffers from low efficiency and poor repetitive detection ability. To overcome the shortcomings of manual inspection, based on machine vision and SVM, an automatic silicon wafer surface defect detection and classification system has been developed in this paper: through feature extraction of color images and defect areas, a series of wafer classifiers are trained and used to separate the defective products from qualified ones. Experiments on samples and actual application in the enterprise show that the system has achieved high accuracy and fast run-time performance, indicating that machine vision is an effective and promising method for polycrystalline silicon solar cell inspection.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 548-549)

Pages:

48-52

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.548-549.48

Citation:

Cite this paper

Online since:

April 2014

Authors:

Han Wang*, Rui Miao, Xiao Song Wu, Wang Yuan Ni, Xu Chen Tang

Keywords:

Automatic Inspection, Machine Vision, Polycrystalline Silicon, Support Vector Machine (SVM), Surface Color Defect

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Quanzhi Wang, Xili Jing: Refractive indexes distribution of anti-reflection coatings for high efficiency silicon solar cells[J], LASER & INFRARED, Vol. 41 (2011), pp.669-672.

Google Scholar

[2] Wujie Zhang, Di Li: Texture defect inspection for silicon solar cell[J], Journal of Computer Applications, Vol. 30 (2010), pp.2702-2705.

DOI: 10.3724/sp.j.1087.2010.02702

Google Scholar

[3] Haijie Sun, Junqiang Lin, et. al: Study on classification decision for polycrystalline silicon solar cell based on color mechanical vision[J], Manufacturing Automation, Vol. 34 (2012), pp.01-04.

Google Scholar

[4] Meilin Feng, Xianjuan Zhou, et. al.: Research of Solar Cell Surface Defect Detection System Based on Machine Vision[J], Advanced Materials Research, Vol. 718-720 (2013), pp.532-536.

DOI: 10.4028/www.scientific.net/amr.718-720.532

Google Scholar

[5] Jun Wang, ShiTong Wang, et al.: Image Thresholding Based on Minimax Probability Criterion[J], PR & AI, Vol. 23, (2010), pp.880-884.

Google Scholar

[6] Yu Zhang, Guangli Chen: Machine Vision Recognition Based on Least Square Support Vector Machine[J], Measurement & Control Technology, Vol. 30 (2011), pp.97-100.

Google Scholar