[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120078-en":3,"doc-seo-120078-105":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},120078,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine learning for early detection of structure loss in the Czochralski process - Thesis","This thesis investigates machine learning techniques for predicting structural loss in the Czochralski process, the industrial standard for producing high-quality mono-crystalline silicon ingots used in solar cells. Three models—logistic regression, random forest, and neural networks—are evaluated across four ingot regions: neck, crown, shoulder, and body. Random forest achieves the strongest overall performance, with the best accuracy, precision, and recall in the neck and crown, while shoulder and body require further refinement. A time-saving random-forest-based approach estimates time saved for remelting operations by 16–21.5 hours on 51 ingots.","Machine learning for early detection of structure loss in the Czochralski process  \nLeander Jacob Nielsen Eiks  \nThesis for Master of Science Degree at the University  \nof Bergen, Norway  \n2024  \n© Copyright Leander Jacob Nielsen Eiks  \nThe material in this publication is protected by copyright law.  \nYear: 2024  \nTitle: Machine learning for early detection of structure  \nloss in the Czochralski process Author: Leander Jacob Nielsen Eiks  \nSupervisor: Martin Møller Greve  \nCo-supervisor: Nello Blaser, Frank Øvstetun, Michael Lindbak  \nAcknowledgements  \nI would like to express my sincere gratitude to my supervisor, Martin Møller Greve, for his support, guidance, and constructive feedback throughout the work on this thesis. His availability at all times, quick responses to my questions, and motivating discussions around the topic were invaluable. I also extend my thanks to Nello Blaser for his crucial guidance in machine learning, especially at the start of this work. His expertise and direction were particularly important in shaping this research.  \nMy heartfelt thanks go to Frank Øvstetun and Michael Lindbak for their extensive knowledge about the Czochralski process and for providing the necessary data. Their willingness to deliver additional data whenever needed and their active interest in the project, demonstrated through regular meetings, greatly contributed to the success of this thesis.  \nI also wish to thank my fellow students for their camaraderie and support throughout this journey. Last but not least, I am deeply grateful to my family for their constant support. Your encouragement and care have been a pillar of strength for me. I would especially like to thank my sister, Linelotte, for her assistance in reviewing the thesis.  \nLeander Jacob Nielsen Eiks Bergen, June 3, 2024  \niv Acknowledgements  \nAbstract  \nThis thesis investigates the use of machine learning techniques to predict structural loss in the Czochralski process. The Czochralski process is the industry standard for producing high-quality mono-crystalline silicon ingots for solar cells. The study evaluates the performance of three machine learning models; logistic regression, random forest, and neural network models across four regions of the ingot: neck, crown, shoulder, and body. The primary goal is to determine which model offers the best predictive performance for early detection of structural loss, thereby enhancing the efficiency and yield of the Czochralski process.  \nThe research reveals that the random forest model consistently delivers the highest accuracy, precision, and recall, especially in the neck and crown regions. This model effectively identifies the early signs of structural loss, making it a valuable tool for improving the process. However, all models faced difficulties in the shoulder and body regions, indicating the need for further refinement and more targeted features.  \nAdditionally, a time-saving model was developed to find time saved during the process by using the random forest model. By maintaining an accuracy threshold of 70%, this model achieved significant time savings, reducing the time required for remelting operations by 16 to 21.5 hours when tested on 51 ingots. These results highlight the potential of machine learning to enhance the Czochralski process, reducing production time and improving the quality of silicon ingots.  \nOverall, the results demonstrate the potential for machine learning to significantly improve the Czochralski process, by enabling early detection of structural loss. Thereby, reducing the time required for remelting operations and enhancing ingot quality.  \nvi Abstract  \nContents  \nAcknowledgements iii  \nAbstract v  \n1 Introduction 1  \n1.1 Background and Motivation .................. 1  \n1.2 Objectives ............................ 3  \n1.3 Contribution ........................... 4  \n1.4 Thesis Outline .......................... 4  \n2 Theoretical Background 5  \n2.1 Semiconductor ....................","cbCaivR5zDvbvPax","https://ap.wps.com/l/cbCaivR5zDvbvPax","pdf",8818721,1,142,"English","en",105,"# Introduction\n## Background and Motivation\n## Objectives\n## Contribution\n## Thesis Outline\n# Theoretical Background\n## Semiconductor\n## Czochralski Process\n## Supervised Learning\n## Logistic Regression\n## Random forest\n## Neural network\n## Hyperparameters tuning\n## Cross validation\n## Model evaluation\n# Introduction to the Data and Datasets\n## Data Overview\n## Data Understanding\n## Labeling\n## Data Preparation\n# Methods and Machine Learning Models\n## Models","[{\"question\":\"Which machine learning models are compared for detecting structural loss?\",\"answer\":\"The study evaluates logistic regression, random forest, and neural network models for predicting structural loss in the Czochralski process.\"},{\"question\":\"How does model performance vary across the ingot regions?\",\"answer\":\"Random forest delivers the highest accuracy, precision, and recall, especially in the neck and crown regions, while the shoulder and body regions are more challenging for all models.\"},{\"question\":\"How is time saving estimated during remelting operations?\",\"answer\":\"A time-saving model based on random forest is developed, using an accuracy threshold of 70% to estimate time saved for remelting, reducing remelting time by 16 to 21.5 hours on 51 ingots.\"}]","Machine learning for early detection of structure loss in the Czochralski process - 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