NDLI: Classification of location of damage in package-on-package (PoP) assemblies using ANN with feature vectors for progression of accrued damage

Content Provider	IEEE Xplore Digital Library
Author	Lall, P. Gupta, P. Goebel, K.
Copyright Year	2012
Description	Author affiliation: Auburn University, Department of Mechanical Engineering, NSF-CAVE3 Electronics Research Center, Auburn, AL 36849 (Lall, P.; Gupta, P.) \|\| NASA Ames Research Center, Moffett Field, CA 94035 (Goebel, K.)
Abstract	Miniaturization of electronic products has resulted in proliferation of package-on-package (PoP) architectures in por table electronics. In this study, daisy-chained double-stack PoP components have been used for early-identification of drop-shock impact damage. Time-spectral feature vector based damage pre-cursors have been identified and measured under app lied shock stimulus. Experimental strain data has been acquired using strain sensors, digital image correlation. Continuity has been measured suing high-speed instrumentation for identification of failure in the PoP assemblies. The timeevolution of spectral content of the damage pre-cursors has been studied using joint time frequency analysis (JTFA). The Karhunen-Loéve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for input to an artificial neural network. The artificial neural net has been trained for failure-mode identification using simulated data-sets created from error-seeded models with specific failure modes. The neural net has then been used to identify and classify the failure modes experimentally observed in tested board assemblies. Supervised learning of multilayer neural net in conjunction with parity has been used to identify the hard-separation boundaries between failure mode clusters in the de-correlated feature space. Pre-failure feature space has been classified for different fault modes in PoP assemblies subjected to drop and shock.
Starting Page	1
Ending Page	16
File Size	2491410
Page Count	16
File Format	PDF
ISBN	9781467303569
e-ISBN	9781467303583
DOI	10.1109/ICPHM.2012.6299535
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PoP Prognostic health management Karhunen Loéve transform Electric shock failure mode classification Vectors Finite element methods Integrated circuit modeling Assembly Substrates neural nets Strain
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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