BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260410T170313Z UID:D78F0F04-8A1C-422B-B308-E1D3AE49EE10 DTSTART;TZID=America/New_York:20260501T110000 DTEND;TZID=America/New_York:20260501T120000 DESCRIPTION:[]This presentation provides a high-level description of CALCE- UMD activities in reliability physics of microelectronic systems\, startin g with a brief history and continuing on to recent trends in multiscale mo deling of the reliability of advanced microelectronic packaging. The discu ssion includes specific focus on the importance of considering material mi crostructure in predictive reliability physics modeling\; and explores the role of reliability physics in the context of AI/ML* approaches for relia bility modeling.\nIn Topic 1\, we will examine three examples where micros tructure-sensitive modeling can provide important insights into material b ehavior: (i) organic interposers/substrates that are based on fabric-reinf orced composites\; (ii) solder alloys with heterogeneous multiscale micros tructure\; (iii) sintered silver materials with agglomerated nanoporous mi crostructure. In Topic 2\, we will qualitatively explore the interplay bet ween reliability physics and AI/ML in influencing both epistemic as well a s aleatory uncertainties in reliability predictions.\n*AI/ML: Artificial I ntelligence / Machine Learning\n\nSpeaker(s): Abhijit Dasgupta\, \n\nBldg: ARMS 3115\, Purdue University\, West Lafayette\, Indiana\, United States\ , Virtual: https://events.vtools.ieee.org/m/554090 LOCATION:Bldg: ARMS 3115\, Purdue University\, West Lafayette\, Indiana\, U nited States\, Virtual: https://events.vtools.ieee.org/m/554090 ORGANIZER:p.wesling@ieee.org SEQUENCE:15 SUMMARY:Reliability Modeling Approaches: Physics or AI/ML URL;VALUE=URI:https://events.vtools.ieee.org/m/554090 X-ALT-DESC:Description: <br /><p><img style="float: right\;" src="https://e vents.vtools.ieee.org/vtools_ui/media/display/7e8dd403-b23a-4f66-b71a-cc8a 8f540170" alt="" width="500" height="250">This presentation provides a hig h-level description of CALCE-UMD activities in reliability physics of micr oelectronic systems\, starting with a brief history and continuing on to r ecent trends in multiscale modeling of the reliability of advanced microel ectronic packaging. The discussion includes specific focus on the importan ce of considering material microstructure in predictive reliability physic s modeling\; and explores the role of reliability physics in the context o f AI/ML* approaches for reliability modeling.<br>In Topic 1\, we will exam ine three examples where microstructure-sensitive modeling can provide imp ortant insights into material behavior: (i) organic interposers/substrates that are based on fabric-reinforced composites\; (ii) solder alloys with heterogeneous multiscale microstructure\; (iii) sintered silver materials with agglomerated nanoporous microstructure. In Topic 2\, we will qualitat ively explore the interplay between reliability physics and AI/ML in influ encing both epistemic as well as aleatory uncertainties in reliability pre dictions.<br><em>*AI/ML: Artificial Intelligence / Machine Learning</em></ p> END:VEVENT END:VCALENDAR