[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128227-en":3,"doc-seo-128227-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128227,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",6,"Technology","Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production - Paper metadata","This paper optimizes cycle time (CT) in semiconductor wafer production, a key driver of operational efficiency and competitiveness in semiconductor manufacturing. A hybrid methodology combines statistical analysis with machine learning to determine the best KPI configuration for individual tools and to minimize CT. Hyperparameter tuning and model optimization are carried out using Sequential Quadratic Programming (SQP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), including constraint handling for KPI limitations via a hierarchical decomposition approach. Experimental results indicate that random forest with GA delivers the strongest CT reduction.","Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production  \nRahman, M. S. , Islam, M. M. M. , Prasad, G. , Bhattacharyya, S. , McCreadie, K. , Reiter, T. , & Parker, N. (2025) . Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production. In 2025 IEEE Conference on Artificial Intelligence (CAI) (pp. 1286-1291) . IEEE. Advance online publication. [https://doi.org/10.1109/CAI64502.2025.00224](https://doi.org/10.1109/CAI64502.2025.00224)  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \n2025 IEEE Conference on Artificial Intelligence (CAI)  \nPublication Status:  \nPublished online: 07/07/2025  \nDOI:  \n10.1109/CAI64502.2025.00224  \nDocument Version  \nAuthor Accepted version  \nFor Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW)  \nWhen citing an AAM published under Ulster University's RRPSW please use the following citation structure:  \nAuthor, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0.  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) .  \nIf the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) .  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 05/08/2026  \nMachine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in  \nSemiconductor Production  \nMohammad Sharifur Rahman Intelligent Systems Research Centre Ulster University Londonderry, BT48 7JL, UK [rahman-m11@ulster.ac.uk](rahman-m11@ulster.ac.uk)  \nKarl McCreadie  \nIntelligent Systems Research Centre Ulster University Londonderry, BT48 7JL, UK [k.mccreadie@ulster.ac.uk](k.mccreadie@ulster.ac.uk)  \nGirijesh Prasad  \nIntelligent Systems Research Centre Ulster University Londonderry, BT48 7JL, UK [g.prasad@ulster.ac.uk](g.prasad@ulster.ac.uk)  \nM.M. Manjurul Islam Intelligent Systems Research Centre Ulster University Londonderry, BT48 7JL, UK[m.islam@ulster.ac.uk](m.islam@ulster.ac.uk)  \nTamas Reiter Seagate Technology Londonderry, Northern Ireland, BT48 0BF, UK [tamas.reiter@seagate.com](tamas.reiter@seagate.com)  \nSaugat Bhattacharyya Intelligent Systems Research Centre Ulster University Londonderry, BT48 7JL, UK [s.bhattacharyya@ulster.ac.uk](s.bhattacharyya@ulster.ac.uk)  \nNuala Parker Seagate Technology Londonderry, Northern Ireland, BT48 0BF, UK [nuala.a.parker@seagate.com](nuala.a.parker@seagate.com)  \nAbstract—This paper aims to optimise cycle time (CT) in semiconductor wafer production, a critical factor for enhancing operational efficiency and competitiveness in the semiconductor manufacturing industry. A hybrid methodology, based on statistical analysis and machine learning (ML) techniques, is developed to identify the optimal combination of key performance indicators (KPIs) for individual tools to minimise CT. To achieve this, hyperparameter tuning","cbCaibNcVd7dfQUa","https://ap.wps.com/l/cbCaibNcVd7dfQUa","pdf",696121,1,7,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## KPI selection and hybrid modeling\n## Hyperparameter tuning and optimization (SQP/PSO/GA)\n## Constraint handling and hierarchical decomposition\n# Experimental results\n## Algorithm comparison for CT reduction","[{\"question\":\"What is the main goal of the proposed framework?\",\"answer\":\"The framework aims to reduce cycle time (CT) in semiconductor wafer production by selecting and optimizing the best KPI combinations for individual tools.\"},{\"question\":\"Which optimization techniques are used to tune and optimize the models?\",\"answer\":\"Model tuning and optimization use Sequential Quadratic Programming (SQP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).\"},{\"question\":\"How do the experiments evaluate performance, and which approach performs best?\",\"answer\":\"Experiments assess CT reduction across techniques and report that random forest with GA significantly outperforms other methods for reducing CT.\"}]","Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production - Paper metadata | PDF",1785945844,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-with-heuristic-search-based-hybrid-framework-for-cycle-time-optimization-in-semiconductor-production-paper-metadata","",{"@graph":36,"@context":86},[37,54,69],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-with-heuristic-search-based-hybrid-framework-for-cycle-time-optimization-in-semiconductor-production-paper-metadata/128227/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the proposed framework?","Question",{"text":76,"@type":77},"The framework aims to reduce cycle time (CT) in semiconductor wafer production by selecting and optimizing the best KPI combinations for individual tools.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which optimization techniques are used to tune and optimize the models?",{"text":81,"@type":77},"Model tuning and optimization use Sequential Quadratic Programming (SQP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).",{"name":83,"@type":74,"acceptedAnswer":84},"How do the experiments evaluate performance, and which approach performs best?",{"text":85,"@type":77},"Experiments assess CT reduction across techniques and report that random forest with GA significantly outperforms other methods for reducing CT.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":107,"slug":138},19,"General","general"]