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Data analytics approach for optimal qualification testing of electronic components

Data analytics approach for optimal qualification testing of electronic components

Stoyanov, Stoyan ORCID: 0000-0001-6091-1226, Ahsan, Mominuil and Bailey, Chris ORCID: 0000-0002-9438-3879 (2018) Data analytics approach for optimal qualification testing of electronic components. In: Proceedings EuroSimE 2018. 2018 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) . IEEE Xplore, pp. 1-9. ISBN 978-1-5386-2358-9 (doi:https://doi.org/10.1109/EuroSimE.2018.8369926)

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Abstract

In electronics manufacturing, required quality of electronic components and parts is ensured through qualification testing using standards and user-defined requirements. The challenge for the industry is that product qualification testing is time-consuming and comes at a substantial cost. The work reported with this paper focus on the development and demonstration of a novel approach that can support “smart qualification testing” by using data analytics and data-driven prognostics modelling.

Data analytics approach is developed and applied to historical qualification test datasets for an electronic module (Device under Test, DUT). The qualification spec involves a series of sequentially performed electrical and functional parameter tests on the DUTs. Data analytics is used to identify the tests that are sensitive to pending failure as well as to cross-evaluate the similarity in measurements between all tests, thus generating also knowledge on potentially redundant tests. The capability of data-driven prognostics modelling, using machine learning techniques and available historical qualification datasets, is also investigated. The results obtained from the study showed that predictive models developed from the identified so-called “sensitive to pending failure” tests feature superior performance compared with conventional, as measured, use of the test data. This work is both novel and original because at present, to the best knowledge of the authors, no similar predictive analytics methodology for qualification test time reduction (respectively cost reduction) is used in the electronics industry.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings EuroSimE 2018
Additional Information: The 19th EuroSimE was held in Toulouse, France, on 15 - 18 April 2018, with technical sponsorship from Institute of Technology Antoine de Saint Exupéry as Local Organiser, and from IEEE-EPS for publishing accepted papers in IEEE Xplore Digital Library.
Uncontrolled Keywords: data analytics, smart test, prognostics, machine learning, electronics product qualification
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG)
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 13 Mar 2019 11:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/19583

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