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KL Transform and Neural-Net Based Framework for Failure Modes Classification in Electronics Subjected to Mechanical-Shock

[+] Author Affiliations
Pradeep Lall, Prashant Gupta

Auburn University, Auburn, AL

Kai Goebel

NASA Ames Research Center, Moffett Field, CA

Paper No. IPACK2011-52198, pp. 563-578; 16 pages
  • ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems
  • ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems, MEMS and NEMS: Volume 1
  • Portland, Oregon, USA, July 6–8, 2011
  • ISBN: 978-0-7918-4461-8
  • Copyright © 2011 by ASME


Electronic systems under extreme shock and vibration environments including shock and vibration may sustain several failure modes simultaneously. Previous experience of the authors indicates that the dominant failure modes experienced by packages in a drop and shock frame work are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loéve transform with parity-based stepwise supervised training of a perceptrons. Early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with Karhunen Loéve transform is new. Feature space has been formed by joint time frequency analysis. Since the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. Error Back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure modes in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hoteling’s T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of specific dominant failure mode and is scalable to system level reliability.

Copyright © 2011 by ASME



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