{"id":321,"date":"2022-12-09T15:38:35","date_gmt":"2022-12-09T14:38:35","guid":{"rendered":"https:\/\/www.mines-stetienne.fr\/fayol\/?page_id=321"},"modified":"2022-12-09T15:38:36","modified_gmt":"2022-12-09T14:38:36","slug":"these-preparee-par-kenneth-ifeanyi-ezukwoke","status":"publish","type":"page","link":"https:\/\/www.mines-stetienne.fr\/fayol\/theses\/these-preparee-par-kenneth-ifeanyi-ezukwoke\/","title":{"rendered":"Th\u00e8se pr\u00e9par\u00e9e par Kenneth-Ifeanyi Ezukwoke"},"content":{"rendered":"\n

Title : Aritificial Intelligence for Failure Analysis decisions-flow in Semiconductor Industry 4.0<\/strong><\/p>\n

Beginning of thesis :<\/strong> 05\/10\/2020
End of thesis :<\/strong> 30\/09\/2023<\/p>\n

Abstract : <\/strong>The Failure Analysis (FA) 4.0 project will address a fundamental challenge for the digital world to ensure that increasingly complex electronic systems operate with complete reliability and safety. Intelligent automation of this analysis decision process using artificial intelligence and machine learning is the objective of the Industry 4.0 consortium; creating a reliable and efficient semiconductor industry.<\/p>\n

This research focuses on developing a comprehensive, central recording analysis of metrology parameters and process-related inhomogeneities, tracked along the production process in correlation with existing failure catalogues. Newly discovered failure mechanisms identified in final components will be explored as part of a novel holistic approach for quality assurance and process improvement. The project is characterised by different research challanges linked with available data and the unexhaustive aspect of human decision-making must be considered with adaptive statistical learning tools.<\/p>\n

Keywords<\/strong> : Failure analysis; Industry 4.0; Natural language processing; Artificial Intelligence; Machine learning ; Decision theory<\/p>\n

Thesis defense : <\/strong> Possibly 06\/12\/2023<\/p>\n

Supervisor :<\/strong><\/p>\n