{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124402"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124402","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automating microscopic image analysis post-photolithography with machine learning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Hardik Sandeep Fulfagar, accepted the attached license on 2024-04-25 at 00:41.","The student, Hardik Sandeep Fulfagar, submitted this Thesis for approval on 2024-04-25 at 00:56.","This Thesis was approved for publication on 2024-04-29 at 10:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20631 on 2024-09-16 at 00:36:59","This thesis addresses the challenges in the manual analysis of semiconductor images by leveraging machine learning techniques to automate the process, with a focus on the photolithography stage. We compare various machine learning techniques for classifying images based on exposure and development levels, aimed at overcoming the limitations of human error in traditional analysis methods. In this thesis, we have created a dataset and used a comprehensive framework for the application of machine learning models. By shifting the focus from merely detecting surface defects to understanding the underlying causes during the photolithography process, this research aims to provide more targeted solutions for improving semiconductor manufacturing and understanding the errors. The findings of this thesis have the potential to enhance methodological precision and efficiency in semiconductor research, particularly beneficial in academic settings for instant analysis and feedback, thereby improving the learning experience for students studying this critical phase of semiconductor manufacturing."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automating microscopic image analysis post-photolithography with machine learning"]}]}],"canonical_facts":{"dc:contributor":["Nahrstedt, Klara"],"dc:creator":["Fulfagar, Hardik Sandeep"],"dc:date":["2024-05","2024-04-29"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Hardik Sandeep Fulfagar, accepted the attached license on 2024-04-25 at 00:41.","The student, Hardik Sandeep Fulfagar, submitted this Thesis for approval on 2024-04-25 at 00:56.","This Thesis was approved for publication on 2024-04-29 at 10:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20631 on 2024-09-16 at 00:36:59","This thesis addresses the challenges in the manual analysis of semiconductor images by leveraging machine learning techniques to automate the process, with a focus on the photolithography stage. We compare various machine learning techniques for classifying images based on exposure and development levels, aimed at overcoming the limitations of human error in traditional analysis methods. In this thesis, we have created a dataset and used a comprehensive framework for the application of machine learning models. By shifting the focus from merely detecting surface defects to understanding the underlying causes during the photolithography process, this research aims to provide more targeted solutions for improving semiconductor manufacturing and understanding the errors. The findings of this thesis have the potential to enhance methodological precision and efficiency in semiconductor research, particularly beneficial in academic settings for instant analysis and feedback, thereby improving the learning experience for students studying this critical phase of semiconductor manufacturing."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124402"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Hardik Sandeep Fulfagar"],"dc:subject":["Machine Learning","Computer Vision","Semiconductor Photolithography","Microscopic Image Analysis Automation","Dataset Creation","Image Classification","Semiconductor Defect Detection"],"dc:title":["Automating microscopic image analysis post-photolithography with machine learning"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}