A data-driven modelling framework for predicting the quality of semiconductor devices to support burn-in decisions.

Ibrahim Ahmed 0009 Politecnico di Milano, Energy Department, Milan, Italy https://orcid.org/0000-0003-2959-6218 Ibrahim Ahmed Ibrahim Ahmed 0001 University of Toronto, Department of Electrical and Computer Engineering, ON, Canada https://orcid.org/0000-0003-2696-3086 https://www.wikidata.org/entity/Q130880087 Ibrahim Ahmed 0002 University of Minnesota, Department of Electrical and Computer Engineering, Minneapolis, MN, USA https://orcid.org/0000-0003-1510-4219 Ibrahim H. I. Ahmed Ibrahim Ahmed 0003 University of the Western Cape, Department of Mathematics and Applied Mathematics, South African National Bioinformatics Institute, Bellville, South Africa Ibrahim Ahmed 0004 Qatar University, College of Engineering, Doha, Qatar Ibrahim Ahmed 0005 Vanderbilt University, Nashville, TN, USA Ibrahim Ahmed 0006 Ibrahim H. Ahmed 0006 University of Edinburgh, School of Informatics, Autonomous Agents Research Group, United Kingdom Ibrahim Ahmed 0007 Groq Inc. Ibrahim Ahmed 0008 Dalhousie University, Department of Pharmacology, Department of Pharmacology, Canada Ibrahim Ahmed 0010 Østfold University College, Fredrikstad, Norway Ibrahim Ahmed 0011 Egypt University of Informatics, New Administrative Capital, Egypt Ibrahim Ahmed 0012 Google Ibrahim Ahmed 0013 Southern Company (United States), Atlanta, GA, USA University of Miami, Department of Industrial and Systems Engineering, Coral Gables, FL, USA https://orcid.org/0000-0003-1364-4131

Ibrahim Ahmed 0009 Piero Baraldi Enrico Zio Horst Lewitschnig A data-driven modelling framework for predicting the quality of semiconductor devices to support burn-in decisions. 111115 2025 204 Comput. Ind. Eng. https://doi.org/10.1016/j.cie.2025.111115 db/journals/candie/candie204.html#AhmedBZL25 streams/journals/candie

Mohammad N. Juybari Ibrahim Ahmed 0009 Piero Baraldi Alejandro Del Cueto Javier Gil Chenyang Lai Sergio Llorente Enrico Zio Estimation of the Case Temperature of Insulated Gate Bipolar Temperatures in Induction Cooktops by Deep Neural Network. 259-263 2022 ICSRS https://doi.org/10.1109/ICSRS56243.2022.10067300 conf/icsrs/2022 db/conf/icsrs/icsrs2022.html#JuybariABCGLLZ22 Piero Baraldi Alejandro Del Cueto Javier Gil Mohammad N. Juybari Chenyang Lai Horst Lewitschnig Sergio Llorente Enrico Zio