Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision

QIAN Xiaoliang; ZHANG Heqing; CHEN Yongxin; ZENG Li; DIAO Zhihua; LIU Yucui; YANG Cunxiang

doi:10.11936/bjutxb2016040063

Volume 43 Issue 1

Sep 2022

Turn off MathJax

Article Contents

JOURNAL OF MECHANICAL ENGINEERING > 2017 > 43(1): 76-85.

QIAN Xiaoliang, ZHANG Heqing, CHEN Yongxin, ZENG Li, DIAO Zhihua, LIU Yucui, YANG Cunxiang. Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision[J]. JOURNAL OF MECHANICAL ENGINEERING, 2017, 43(1): 76-85. doi: 10.11936/bjutxb2016040063

Citation:

QIAN Xiaoliang, ZHANG Heqing, CHEN Yongxin, ZENG Li, DIAO Zhihua, LIU Yucui, YANG Cunxiang. Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision[J]. JOURNAL OF MECHANICAL ENGINEERING, 2017, 43(1): 76-85. doi: 10.11936/bjutxb2016040063

Citation:

QIAN Xiaoliang, ZHANG Heqing, CHEN Yongxin, ZENG Li, DIAO Zhihua, LIU Yucui, YANG Cunxiang. Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision[J]. JOURNAL OF MECHANICAL ENGINEERING, 2017, 43(1): 76-85. doi: 10.11936/bjutxb2016040063

PDF( 0 KB)

Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision

doi: 10.11936/bjutxb2016040063

1.
College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2.
Xuji Croup Corporation, Xuchang 461000, Henan,China

Received Date: 18 Apr 2016
Available Online: 09 Sep 2022
Issue Publish Date: 01 Jan 2017

Abstract

Abstract

Considering the advantages of simple operation and high detecting accuracy, all aspects involved in solar cell surface defect detection methods based on machine vision were reviewed in this paper. First of all, the various imaging techniques and common defect types of solar cells surface were summarized. Secondly, the existing detection methods were introduced and compared with each other according to the different idea of mathematical modeling. Finally, a brief summary of this article and perspective of future research are presented. It can be concluded that the solar cell surface defect detection methods based on machine vision have made great progress. However, there is still room for improvement in algorithm design of feature extraction, such as feature extraction algorithm based on deep neural networks.
- solar cell,
- machine vision,
- surface defect,
- imaging

FullText(HTML)

References(54)

References

[1]	CARCIA P F, MCLEAN R S, HEGEDUS S.ALD moisture barrier for Cu(InGa)Se2 solar cells[J]. Ecs Transactions, 2010, 33(2): 237-243.
[2]	DUENAS S, PEREZ E, CASTAN H, et al.The role of defects in solar cells: control and detection defects in solar cells[C]//The Spanish 2013 Conference on Electron Devices (CDE). New York: IEEE, 2013: 301-304.
[3]	ISTROV A A, HIESLMAIR H, VYVENKO O F, et al.Defect recognition and impurity detection techniques in crystalline silicon for solar cells[J]. Solar Energy Materials & Solar Cells, 2002, 72(1): 441-451.
[4]	TSUZUKI K M, TSUTOMU Y, TAKEHIO T, et al. Inspection method and production method of solar cell module: US6271462B1[P].2001-08-07.
[5]	ESQUIVEL O. Contrast imaging method for inspecting specular surface devices: US6433867B1[P].2002-08-13.
[6]	SAWYER D E, KESSLER H K.Laser scanning of solar cells for the display of cell operating characteristics and detection of cell defects[J]. IEEE Transactions on Electron Devices, 1980, 27(4): 864-872.
[7]	CHEN X Y, PEDERSEN A, HELLESØ O G, et al.Electrical noise of laser diodes measured over a wide range of bias currents[J]. Microelectronics Reliability, 2000, 40(11): 1925-1928.
[8]	BELYAEV A, POLUPAN O, OSTAPENKO S, et al.Resonance ultrasonic vibration diagnostics of elastic stress in full-size silicon wafers[J]. Semiconductor Science and Technology, 2006, 21(3): 254.
[9]	BYELYAYEV A.Stress diagnostics and crack detection in full-size silicon wafers using resonance ultrasonic vibrations[D]. Tampa Bay: University of South Florida, 2005.
[10]	OSTAPENKO S, DALLAS W, HESS D, et al.Crack detection and analyses using resonance ultrasonic vibrations in crystalline silicon wafers[C]//The 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion. New York: IEEE, 2006: 920-923.
[11]	DALLAS W, POLUPAN O, OSTAOENKO S.Resonance ultrasonic vibrations for crack detection in photovoltaic silicon wafers[J]. Measurement Science & Technology, 2007, 18(3): 852-858.
[12]	ZHANG X, HU J, WU Y, et al.Direct observation of defects in triple-junction solar cell by optical deep-level transient spectroscopy[J]. Journal of Physics D: Applied Physics, 2009, 42(14): 145401-145405.
[13]	WEN T K, YIN C C.Crack detection in photovoltaic cells by interferometric analysis of electronic speckle patterns[J]. Solar Energy Materials & Solar Cells, 2011, 98(5): 216-223.
[14]	FUYUKI T, KITIYANAN A.Photographic diagnosis of crystalline silicon solar cells utilizing electroluminescence[J]. Applied Physics A: Materials Science & Processing, 2009, 96(1): 189-196.
[15]	XU P, ZHOU W J, FEI M R.Detection methods for micro-cracked defects of photovoltaic modules based on machine vision[C]//The 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS). New York: IEEE, 2014: 609-613.
[16]	TSAI D M, WU S C, LI W C.Defect detection of solar cells in electroluminescence images using Fourier image reconstruction[J]. Solar Energy Materials & Solar Cells, 2012, 99(99): 250-262.
[17]	TAKAHASHI Y, KAJI Y, OGANE A, et al.“Luminoscopy”-novel tool for the diagnosis of crystalline silicon solar cells and modules utilizing electroluminescence[C]//The 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion. New York: IEEE, 2006: 924-927.
[18]	GABOR A M, RALLI M, MONTMINY S, et al.Soldering induced damage to thin Si solar cells and detection of cracked cells in modules[C]//The 21st European Photovoltaic Solar Energy Conference. New York: IEEE, 2006: 4-8.
[19]	CHATURVEDI P, HOEX B, WALSH T M.Broken metal fingers in silicon wafer solar cells and PV modules[J]. Solar Energy Materials & Solar Cells, 2013, 108(1): 78-81.
[20]	DEMANT M R S, KRISCH J, SCHOENFELDER S, et al. Detection and analysis of micro-cracks in multi-crystalline silicon wafers during solar cell production[C]//The 2011 37th IEEE Conference on Photovoltaic Specialists Conference (PVSC) . New York: IEEE, 2011: 001641-001646.
[21]	OLSEN E, FL A S.Spectral and spatially resolved imaging of photoluminescence in multicrystalline silicon wafers[J]. Applied Physics Letters, 2011, 99(1): 011903-011903-3.
[22]	SUN Q, MELNIKOV A. MANDELIS A.Camera-based high frequency heterodyne lock-in carrierographic (frequency-domain photoluminescence) imaging of crystalline silicon wafers[J]. Physica Status Solidi(a), 2016, 213(2): 405-411.
[23]	CHEN J T, KE S S, LIN K W, et al.High-performance inspecting system for detecting micro-crack defects of solar wafer[C]//The 2010 IEEE Conference on Sensors. New York: IEEE, 2010: 494-497.
[24]	CHIOU Y C, LIU J, LIANG Y T.Micro crack detection of multi-crystalline silicon solar wafer using machine vision techniques[J]. Sensor Review, 2011, 31(2): 154-165.
[25]	BROOKS W S M, LAMB D A, IRVINE S J C. IR reflectance imaging for crystalline Si solar cell crack detection[J]. IEEE Journal of Photovoltaics, 2015, 5(5): 1271-1275.
[26]	KIM G B.Micro defect detection in solar cell wafer based on hybrid illumination and near-infrared optics[C] ∥The 2013 9th Asian Control Conference(ASCC) . New York: IEEE, 2013: 1-5.
[27]	MAHDAVIPOUR Z, ABDULLAH M Z.Micro-crack detection of polycrystalline silicon solar wafer[J]. Iete Technical Review, 2015, 32(6): 1-7.
[28]	KO S S, LIU C S, LIN Y C.Optical inspection system with tunable exposure unit for micro-crack detection in solar wafers[J]. Optik - International Journal for Light and Electron Optics, 2013, 124(124): 4030-4035.
[29]	TEO T W, MAHDAVIPOUR Z, ABDULLAH M Z.High-speed micro-crack detection of solar wafers with variable thickness[C]//The 2014 IEEE International Conference on Imaging Systems and Techniques (IST) . New York: IEEE, 2014: 237-241.
[30]	DONG D, CHEN G M.A method of silicon solar cells defect detection based on near-infrared images[J]. Information & Electronic Engineering, 2010, 8(5): 539-543. (in Chinese)
[31]	WANG N.Silicon solar cell based on visual detection method[D]. Baoding: Agricultural University of Hebei, 2014. (in Chinese)
[32]	MINKEVI<inline-graphic href="0254-0037-43-1-76/img_6.jpg"/>IUS L, SUZANOVIC<inline-graphic href="0254-0037-43-1-76/img_7.jpg"/>IEN<inline-graphic href="0254-0037-43-1-76/img_8.jpg"/> R, BALAKAUSKAS S, et al. Detection of tab wire soldering defects on silicon solar cells using terahertz time-domain spectroscopy[J]. Electronics Letters, 2012, 48(15): 932-934.
[33]	JEN C Y, RICHTER C.Doping profile recognition applied to silicon photovoltaic cells using terahertz time-domain spectroscopy[J]. IEEE Transactions on Terahertz Science and Technology, 2014, 4(5): 560-567.
[34]	ABBOTT M, COUSINS P, CHEN F, et al.Laser-induced defects in crystalline silicon solar cells[C]//The Thirty-first IEEE Conference on Photovoltaic Specialists. New York: IEEE, 2005: 1241-1244.
[35]	KONTGES M, KUNZE I, KAIARI S S, et al.The risk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks[J]. La Medicina Del Lavoro, 2011, 95(4): 1131-1137.
[36]	WANG H.Polysilicon solar wafer surface defect detection and the design and development of software inspection system[D]. Shanghai: Shanghai Jiao Tong University, 2014. (in Chinese)
[37]	ANWAR S A. ABDULLAH M Z.Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique[J]. Eurasip Journal on Image & Video Processing, 2014, 2014(1): 1-17.
[38]	TSAI D M.Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion[J]. Image and Vision Computing, 2010, 28(3): 491-501.
[39]	BAKALEXIS S A, BOUTALIS Y S, MERTZIOS B G.Edge detection and image segmentation based on nonlinear anisotropic diffusion[C]//The 2002 14th International Conference on Digital Signal Processing. New York: IEEE, 2002: 1203-1206.
[40]	TSAI D M, LUO J Y.Mean shift-based defect detection in multicrystalline solar wafer surfaces[J]. IEEE Transactions on Industrial Informatics, 2011, 7(1): 125-135.
[41]	AGHAMOHAMMADI A H, PRABUWONO A S.Solar cell panel crack detection using particle swarm optimization algorithm[C]//The 2011 International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR). Putrajaya: IEEE, 2011: 160-164.
[42]	KENNEDY J, EBERHART R.Particle swarm optimization[C]//The IEEE International Conference on Neural Networks. New York: IEEE, 1995: 1942-1948.
[43]	FU Z, ZHAO Y, LIU Y, et al.Solar cell crack inspection by image processing[C]//The 2004 International Conference on Business of Electronic Product Reliability and Liability. New York: IEEE, 2004: 77-80.
[44]	TSAI D M, LI G N, LI W C, et al.Defect detection in multi-crystal solar cells using clustering with uniformity measures[J]. Advanced Engineering Informatics, 2015, 29(3): 419-430.
[45]	LIW C, TSAI D M.Wavelet-based defect detection in solar wafer images with inhomogeneous texture[J]. Pattern Recognition, 2012, 45(2): 742-756.
[46]	WANG Z.Research on detection technology for solar cells multi-defects in complicated background[J]. Journal of Information & Computational Science, 2014, 11(2): 449-459.
[47]	LU C J, TSAI D M.Automatic defect inspection for LCDs using singular value decomposition[J]. International Journal of Advanced Manufacturing Technology, 2005, 25(1): 53-61.
[48]	YAO M H, LI J, WANG X B.Solar cell surface defects detection using RPCA method[J]. Chinese Journal of Computers, 2013, 36(9): 1943-1952. (in Chinese)
[49]	DEMANT M, WELSCHEHOLD T, OSWALD M, et al.Microcracks in silicon wafers Ⅰ: inline detection and implications of crack morphology on wafer strength[J]. IEEE Journal of Photovoltaics, 2016, 6(1): 1-10.
[50]	GONG F, ZHANG X W, SUN H.Detection system for solar module surface defects based on constrained ICA model and PSO method[J]. Acta Optica Sinica, 2012(4): 169-177. (in Chinese)
[51]	TSAI D M, WU S C, CHIU W Y.Defect detection in solar modules using ICA basis images[J]. IEEE Transactions on Industrial Informatics, 2013, 9(1): 122-131.
[52]	JENSSEN R, ELTOFT T.Independent component analysis for texture segmentation[J]. Pattern Recognition, 2003, 36(10): 2301-2315.
[53]	WANG X B, LI J, YAO M H, et al.Solar cells surface defects detection based on deep learning[J]. PR&AI, 2014, 27(6): 517-523. (in Chinese)
[54]	ZHAO R, OUYANG W, LI H, et al.Saliency detection by multi-context deep learning[C]//The 2015 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 1265-1274.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

Figures(5) / Tables(2)

Get Citation

PDF

XML

Article Metrics

Article views(365) PDF downloads(0)

Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision

doi: 10.11936/bjutxb2016040063

Abstract

References

Proportional views

Catalog

Article Metrics

Proportional views

Related

Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision

doi: 10.11936/bjutxb2016040063

Abstract

References

Proportional views

Catalog

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content