[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117826-en":3,"doc-seo-117826-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117826,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine learning for advanced characterisation of silicon solar cells - Thesis","Improving photovoltaic efficiency, reliability, and durability accelerates the transition to a carbon-free society. With tens of millions of solar cells manufactured daily, this PhD thesis leverages available characterisation data to detect defects using advanced machine learning. It models recombination statistics from temperature- and injection-dependent lifetime data, enables luminescence-based end-of-line binning, and correlates efficiency with luminescence images. Transfer learning supports half-cut and shingled cells, while a framework performs automated efficiency-loss analysis and uses a genetic algorithm to optimize process parameters for maximum efficiency.","Machine learning for advanced characterisation of silicon solar cells  \nAuthor:  \nBuratti , Yoann  \nPublication Date:  \n2023  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/101122](http://hdl.handle. net/1959.4/101122) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2023-04-16  \nMachine learning for advanced characterisation of silicon solar cells  \nYoann Buratti  \nA thesis in fulfillment of the requirements for the degree of Doctor of Philosophy  \nSchool of Photovoltaic and Renewable Energy Engineering Faculty of Engineering  \nOriginality, Copyright and Authenticity Statements  \nInclusion of Publications Statement  \nMerci maman et mémé de m’avoir enseigné qu’avec volonté et ténacité, même les objectifs les plus fous sont à ma portée.  \nAbstract  \nImproving the efficiency, reliability, and durability of photovoltaic cells and modules is key to accelerating the transition towards a carbon-free society. With tens of millions of solar cells manufactured every day, this thesis aims to leverage the available characterisation data to identify defects in solar cells using powerful machine learning techniques. Firstly, it explores temperature and injection dependent lifetime data to characterise bulk defects in silicon solar cells. Machine learning algorithms were trained to model the recombination statistics’ inverse function and predict the defect parameters. The proposed image representation of lifetime data and access to powerful deep learning techniques surpasses traditional defect parameter extraction techniques and enables the extraction of temperature dependent defect parameters. Secondly, it makes use of end-of-line current-voltage measurements and luminescence images to demonstrate how luminescence imaging can satisfy the needs of end-of-line binning. By introducing a deep learning framework, the cell efficiency is correlated to the luminescence image and shows that a luminescence-based binning does not impact the mismatch losses of the fabricated modules while having a greater capability of detecting defects in solar cells. The framework is shown in multiple transfer learning and fine-tuning applications such as half-cut and shingled cells. The method is then extended for automated efficiency-loss analysis, where a new deep learning framework identifies the defective regions in the luminescence image and their impact on the overall cell efficiency. Finally, it presents a machine learning algorithm to model the relationship between input process parameters and output efficiency to identify the recipe for achieving the highest solar cell efficiency with the help of a genetic algorithm optimiser.  \nThe development of machine learning-powered characterisation truly unlocks new insight and brings the photovoltaic industry to the next level, making the most of the available data to accelerate the rate of improvement of solar cell and module efficiency while identifying the potential defects impacting their reliability and durability.  \nAcknowledgements  \nThis endeavour would not have been possible without the support and guidance of my supervisor, Ziv Hameiri. My deepest and sincerest thanks and appreciation for all you have done along the Ph.D. journey. The constant discussion and exchange of ideas really shaped the following thesis, while the group that you built over the years has fostered excellent research practices as well as a lively group to spend quality time with. Thankyou for spending the hours reading and re-reading my abstracts, papers, and thesis chapters; your feedback was always constructive and valuable. Thank you for your honesty, your accessibility, and for always having the time for your students. Those are the greatest qualities one can ask for in a supervisor.  \nMy sincere gratitude to my joint ","cbCaiiO5OgMkPGBQ","https://ap.wps.com/l/cbCaiiO5OgMkPGBQ","pdf",9525750,1,147,"English","en",105,"# Abstract\n## Temperature- and injection-dependent lifetime modelling\n## Luminescence imaging for end-of-line binning\n## Efficiency-loss and defective region analysis\n## Process-parameter optimization with genetic algorithms","[{\"question\":\"What main problem does the thesis address for silicon solar cells?\",\"answer\":\"It focuses on improving photovoltaic efficiency, reliability, and durability by using characterisation data to identify defects in solar cells through machine learning.\"},{\"question\":\"How does the thesis use lifetime data to characterize bulk defects?\",\"answer\":\"It trains machine learning algorithms to model the inverse of recombination statistics and predict defect parameters, enabling extraction of temperature-dependent defect parameters.\"},{\"question\":\"How is luminescence imaging used for end-of-line binning and efficiency analysis?\",\"answer\":\"A deep learning framework correlates cell efficiency to luminescence images, supports luminescence-based end-of-line binning, and extends to automated efficiency-loss analysis by identifying defective regions.\"}]","Machine learning for advanced characterisation of silicon solar cells - Thesis | PDF",1785679835,370,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-advanced-characterisation-of-silicon-solar-cells-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-advanced-characterisation-of-silicon-solar-cells-thesis/117826/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What main problem does the thesis address for silicon solar cells?","Question",{"text":75,"@type":76},"It focuses on improving photovoltaic efficiency, reliability, and durability by using characterisation data to identify defects in solar cells through machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis use lifetime data to characterize bulk defects?",{"text":80,"@type":76},"It trains machine learning algorithms to model the inverse of recombination statistics and predict defect parameters, enabling extraction of temperature-dependent defect parameters.",{"name":82,"@type":73,"acceptedAnswer":83},"How is luminescence imaging used for end-of-line binning and efficiency analysis?",{"text":84,"@type":76},"A deep learning framework correlates cell efficiency to luminescence images, supports luminescence-based end-of-line binning, and extends to automated efficiency-loss analysis by identifying defective regions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]