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Failure-Mode Clustering for Electronics Under Shock and Vibration Using Neural-Net Based Self-Organized Maps

[+] Author Affiliations
Pradeep Lall, Prashant Gupta

Auburn University, Auburn, AL

Paper No. IMECE2010-40666, pp. 517-531; 15 pages
doi:10.1115/IMECE2010-40666
From:
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 4: Electronics and Photonics
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4428-1
  • Copyright © 2010 by ASME

abstract

In this paper prognostic framework for electronic systems has been developed with neural network based self organizing maps for clustering of failure modes. The presented approach resides in the pre-failure space with a focus on electronic systems with multiple failure modes. Portable electronic products subjected to transient shock may exhibit multiple failure modes in vicinity of the interconnects. Failure is often diagnosed by loss of functionality using techniques including the built-in self test, which provides limited insight into reliability and remaining useful life. Unsupervised learning of the neural net has been used to train the neural net for identification of individual failure modes. Feature vectors have been developed based on damage pre-cursors from time-spectral measurements. The clustered damage pre-cursors have been correlated with failure modes of the underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC305, SAC405 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data. Activation of different neurons in the lattice for various failure modes and combinations of failure modes has been demonstrated. Previously the authors have developed techniques based on statistical pattern recognition for leading indication of impending failure and detection of damage initiation and progression [Lall 2006a , 2007a , 2008]. Early classification of multiple failure modes in the pre-failure space is new.

Copyright © 2010 by ASME

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