Ahsan, Mominul, Stoyanov, Stoyan ORCID: https://orcid.org/0000-0001-6091-1226 and Bailey, Christopher ORCID: https://orcid.org/0000-0002-9438-3879 (2018) Data driven prognostics for failure of power semiconductor packages. In: Proceedings of 41st Spring Seminar on Electronics Technology (ISSE). IEEE Xplore, pp. 1-6. ISBN 978-1-5386-5730-0 ISSN 2161-2536 (Online) (doi:10.1109/ISSE.2018.8443612)

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Abstract

Power chips such as Metal Oxide Field Effect Transistors (MOSFETs) are widely used and can be found in many electronics and electrical products. The ability to predict the degradation of such power electronic devices can minimise the risk of their failure during operation and support maintenance planning operations. In this study, a data driven prognostics approach using system identification and machine learning modelling technique is developed and used to predict the time-to-failure of MOSFET TO-220 packages associated with delamination failure mode of the die attachment. Run-to-failure data under thermal overstress loading conditions for power chip devices, available from the NASA Prognostics Centre data repository, is used to develop a data-driven prognostic model that can be used to predict the time-to-failure (TtF) of power MOSFETs under accelerated test loads. An increment in ON-state resistance of the MOSFET is used as precursor for device failure through die-attach degradation. Results from this research show that when monitored data from a damage indicator for a particular failure mode of an electronic package changes dynamically, data-driven modelling using engineering control techniques such as State-Space representation is capable of producing reliable, multi-step ahead predictions for the time-to-failure of the device.

Item Type:	Conference Proceedings
Title of Proceedings:	Proceedings of 41st Spring Seminar on Electronics Technology (ISSE)
Additional Information:	Conference held at Zlatibor, Serbia, 16-20 May 2018.
Uncontrolled Keywords:	Data driven prognostics; Power semiconductor packages; Machine learning; Reliability test; Failure
Subjects:	Q Science > QA Mathematics
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/20231

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