{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:20:45Z","timestamp":1782969645516,"version":"3.54.5"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T00:00:00Z","timestamp":1599264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T00:00:00Z","timestamp":1599264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010672","name":"H2020 LEIT Advanced Manufacturing and Processing","doi-asserted-by":"publisher","award":["679692"],"award-info":[{"award-number":["679692"]}],"id":[{"id":"10.13039\/100010672","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Solar cells represent one of the most important sources of clean energy in modern societies. Solar cell manufacturing is a delicate process that often introduces defects that reduce cell efficiency or compromise durability. Current inspection systems detect and discard faulty cells, wasting a significant percentage of resources. We introduce<jats:italic>Cell Doctor<\/jats:italic>, a new inspection system that uses state of the art techniques to locate and classify defects in solar cells and performs a diagnostic and treatment process to isolate or eliminate the defects.<jats:italic>Cell Doctor<\/jats:italic>uses a fully automatic process that can be included in a manufacturing line. Incoming solar cells are first moved with a robotic arm to an Electroluminescence diagnostic station, where they are imaged and analysed with a set of Gabor filters, a Principal Component Analysis technique, a Random Forest classifier and different image processing techniques to detect possible defects in the surface of the cell. After the diagnosis, a laser station performs an isolation or cutting process depending on the detected defects. In a final stage, the solar cells are characterised in terms of their I\u2013V Curve and I\u2013V Parameters, in a Solar Simulator station. We validated and tested<jats:italic>Cell Doctor<\/jats:italic>with a labelled dataset of images of monocrystalline silicon cells, obtaining an accuracy and recall above 90% for<jats:italic>Cracks<\/jats:italic>,<jats:italic>Area Defects<\/jats:italic>and<jats:italic>Finger interruptions<\/jats:italic>; and precision values of 77% for Finger Interruptions and above 90% for<jats:italic>Cracks<\/jats:italic>and<jats:italic>Area Defects.<\/jats:italic>Which allows<jats:italic>Cell Doctor<\/jats:italic>to diagnose and repair solar cells in an industrial environment in a fully automatic way.<\/jats:p>","DOI":"10.1007\/s10845-020-01642-6","type":"journal-article","created":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T20:07:43Z","timestamp":1599336463000},"page":"1163-1172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automatic solar cell diagnosis and treatment"],"prefix":"10.1007","volume":"32","author":[{"given":"Alvaro","family":"Rodriguez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2760-1650","authenticated-orcid":false,"given":"Carlos","family":"Gonzalez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andres","family":"Fernandez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamara","family":"Delgado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Bellman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,5]]},"reference":[{"issue":"1","key":"1642_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/1687-5281-2014-15","volume":"2014","author":"SA Anwar","year":"2014","unstructured":"Anwar, S. A., & Abdullah, M. Z. (2014). Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. EURASIP Journal on Image and Video Processing, 2014(1), 15.","journal-title":"EURASIP Journal on Image and Video Processing"},{"issue":"1","key":"1642_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"issue":"7","key":"1642_CR3","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1002\/pip.544","volume":"12","author":"O Breitenstein","year":"2004","unstructured":"Breitenstein, O., Rakotoniaina, J. P., Al Rifai, M. H., & Werner, M. (2004). Shunt types in crystalline silicon solar cells. Progress in Photovoltaics: Research and Applications, 12(7), 529\u2013538.","journal-title":"Progress in Photovoltaics: Research and Applications"},{"key":"1642_CR4","first-page":"1","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen, H., Pang, Y., Hu, Q., & Liu, K. (2018). Solar cell surface defect inspection based on multispectral convolutional neural network. Journal of Intelligent Manufacturing, 23, 1\u201316.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"1642_CR5","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1108\/02602281111110013","volume":"31","author":"Y-C Chiou","year":"2011","unstructured":"Chiou, Y.-C., Liu, J.-Z., & Liang, Y.-T. (2011). Micro crack detection of multi-crystalline silicon solar wafer using machine vision techniques. Sensor Review, 31(2), 154\u2013165.","journal-title":"Sensor Review"},{"key":"1642_CR6","unstructured":"Correia, S. A., Lossen, J., & B\u00e4hr, M. (2006). Eliminating shunts from industrial silicon solar cells by spatially resolved analysis. In Proceedings of the 21st European photovoltaic solar energy conference (pp. 1297\u20131300)."},{"key":"1642_CR7","doi-asserted-by":"crossref","unstructured":"Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C. (2018). Automatic classification of defective photovoltaic module cells in electroluminescence images. arXiv preprint http:\/\/arxiv.org\/abs\/1807.02894.","DOI":"10.1016\/j.solener.2019.02.067"},{"issue":"12","key":"1642_CR8","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1364\/JOSAA.4.002379","volume":"4","author":"DJ Field","year":"1987","unstructured":"Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Josa a, 4(12), 2379\u20132394.","journal-title":"Josa a"},{"key":"1642_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01495-8","author":"C Gonzalez-Val","year":"2019","unstructured":"Gonzalez-Val, C., Pallas, A., Panadeiro, V., & Rodriguez, A. (2019). A convolutional approach to quality monitoring for laser manufacturing. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-019-01495-8.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1642_CR10","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1109\/TPAMI.1987.4767964","volume":"5","author":"J Illingworth","year":"1987","unstructured":"Illingworth, J., & Kittler, J. (1987). The adaptive Hough transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 690\u2013698.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"1642_CR11","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.patcog.2011.07.025","volume":"45","author":"W-C Li","year":"2012","unstructured":"Li, W.-C., & Tsai, D.-M. (2012). Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recognition, 45(2), 742\u2013756.","journal-title":"Pattern Recognition"},{"issue":"11","key":"1642_CR12","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1364\/JOSA.70.001297","volume":"70","author":"S Mar\u0109elja","year":"1980","unstructured":"Mar\u0109elja, S. (1980). Mathematical description of the responses of simple cortical cells. JOSA, 70(11), 1297\u20131300.","journal-title":"JOSA"},{"key":"1642_CR13","unstructured":"Masson, G., & Brunisholz, M. (2016). Snapshot of global photovoltaic markets 1992\u20132015. International Energy Agency, Tech. Rep. T1-29, 2016."},{"issue":"11","key":"1642_CR14","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.imavis.2012.06.010","volume":"30","author":"DK Prasad","year":"2012","unstructured":"Prasad, D. K., Leung, M. K. H., Quek, C., & Cho, S.-Y. (2012). A novel framework for making dominant point detection methods non-parametric. Image and Vision Computing, 30(11), 843\u2013859.","journal-title":"Image and Vision Computing"},{"issue":"7","key":"1642_CR15","first-page":"1048","volume":"13","author":"X Qian","year":"2017","unstructured":"Qian, X., Zhang, H. H., Zhang, H. H., Wu, Y., Diao, Z., Wu, Q.-E., et al. (2017). Solar cell surface defects detection based on computer vision. International Journal of Performability Engineering, 13(7), 1048.","journal-title":"International Journal of Performability Engineering"},{"key":"1642_CR17","doi-asserted-by":"publisher","unstructured":"Rodr\u00edguez, A. (2014). A methodology to develop computer vision systems in civil engineering: Applications in material testing and fish tracking. University of A Coru\u00f1a (Doctoral dissertation). Retrieved from https:\/\/doi.org\/10.13140\/RG.2.2.17903.74401","DOI":"10.13140\/RG.2.2.17903.74401"},{"key":"1642_CR16","doi-asserted-by":"crossref","unstructured":"Rodriguez-Araujo, J., & Garcia-Diaz, A. (2014). Automated in-line defect classification and localization in solar cells for laser-based repair. In 2014 IEEE 23rd international symposium on industrial electronics (ISIE), (pp. 1099\u20131104).","DOI":"10.1109\/ISIE.2014.6864767"},{"key":"1642_CR18","unstructured":"Schmauder, J., Kopecek, R., Barinka, R., Barinkova, P., Bollar, A., Koumanakos, D. et al. (2012). First steps towards an automated repairing of solar cells by laser enabled silicon post-processing. In Proc. EU-PVSEC."},{"issue":"1","key":"1642_CR19","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TII.2012.2209663","volume":"9","author":"D-M Tsai","year":"2013","unstructured":"Tsai, D.-M., Wu, S.-C., & Chiu, W.-Y. (2013). Defect detection in solar modules using ICA basis images. IEEE Transactions on Industrial Informatics, 9(1), 122\u2013131.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1642_CR20","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.solmat.2011.12.007","volume":"99","author":"D-M Tsai","year":"2012","unstructured":"Tsai, D.-M., Wu, S.-C., & Li, W.-C. (2012). Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Solar Energy Materials and Solar Cells, 99, 250\u2013262.","journal-title":"Solar Energy Materials and Solar Cells"},{"key":"1642_CR21","doi-asserted-by":"crossref","unstructured":"Tseng, D.-C., Liu, Y.-S., & Chou, C.-M. (2015). Automatic finger interruption detection in electroluminescence images of multicrystalline solar cells. Mathematical Problems in Engineering, 2015.","DOI":"10.1155\/2015\/879675"},{"key":"1642_CR22","first-page":"2","volume":"7","author":"Q Xiaoliang","year":"2017","unstructured":"Xiaoliang, Q., Heqing, Z., Huanlong, Z., Zhendong, H., & Cunxiang, Y. (2017). Solar cell surface defect detection based on visual saliency. Chinese Journal of Scientific Instrument, 7, 2.","journal-title":"Chinese Journal of Scientific Instrument"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01642-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-020-01642-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01642-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T22:25:23Z","timestamp":1696631123000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-020-01642-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,5]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1642"],"URL":"https:\/\/doi.org\/10.1007\/s10845-020-01642-6","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,5]]},"assertion":[{"value":"31 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}