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Research Papers

Prognostication Based on Resistance-Spectroscopy and Phase-Sensitive Detection for Electronics Subjected to Shock-Impact

[+] Author and Article Information
Pradeep Lall

Department of Mechanical Engineering, NSF Center for Advanced Vehicle and Extreme Environment Electronics (CAVE3 ),  Auburn University, Auburn, AL 36849lall@eng.auburn.edu

Ryan Lowe

Department of Mechanical Engineering, NSF Center for Advanced Vehicle and Extreme Environment Electronics (CAVE3 ),  Auburn University, Auburn, AL 36849

Kai Goebel

 Prognostics Center of Excellence, NASA Ames Research Center, Moffett Field, CA 94035

J. Electron. Packag 134(2), 021001 (Jun 11, 2012) (10 pages) doi:10.1115/1.4006706 History: Received December 25, 2010; Revised March 12, 2012; Published June 11, 2012; Online June 11, 2012

Leading indicators of failure have been developed based on high-frequency characteristics, and system-transfer function derived from resistance spectroscopy measurements during shock and vibration. The technique is intended for condition-monitoring in high-reliability applications where the knowledge of impending failure is critical and the risks in terms of loss-of-functionality are too high to bear. Previously, resistance spectroscopy measurements have been used during thermal cycling tests to monitor damage progression due to thermomechanical stresses. The development of resistance spectroscopy based damage precursors for prognostication under shock and vibration is new. In this paper, the high-frequency characteristics and system-transfer function based on resistance spectroscopy measurements have been correlated to the damage progression in electronics during shock and vibration. Packages being examined include ceramic area-array packages. Second level interconnect technologies examined include copper-reinforced solder column, SAC305 solder ball, and 90Pb10Sn high-lead solder ball. Assemblies have been subjected to 1500 g, 0.5 ms pulse (JESD-B2111). Continuity has been monitored in situ during the shock test for identification of part-failure. Resistance spectroscopy based damage precursors have been correlated to the optically measured transient strain based feature vectors. High speed cameras have been used to capture the transient strain histories during shock-impact. Statistical pattern recognition techniques have been used to identify damage initiation and progression and determine the statistical significance in variance between healthy and damaged assemblies. Models for healthy and damaged packages have been developed based on package characteristics. Data presented show that high-frequency characteristics and system-transfer characteristics based on resistance spectroscopy measurements can be used for condition-monitoring, damage initiation, and progression in electronic systems. A positive prognostic distance has been demonstrated for each of the interconnect technologies tested.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 2

Interconnect array configuration for test board B

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Figure 4

Schematic representation of solder ball denoting length and cross-sectional area

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Figure 5

Wheatstone bridge with capacitors C1 and C2 and resistors R1, R2, R3, and PKG

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Figure 6

Inputs and outputs for resistance spectroscopy setup

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Figure 7

Theoretical bode plot of transfer function expected from resistance spectroscopy

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Figure 8

Bode plots of theoretical transfer function. Arrows indicate trends of increasing resistance of the package.

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Figure 9

Comparison of theoretically derived transfer function and experimentally measure results for a healthy package (test board A, SAC (SAC305) alloy, and package U1)

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Figure 10

(a) Drop tower and high speed digital cameras for digital image correlation; (b) Lansmont Model 23 Shock Test System

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Figure 11

Failure metric calculation from electrical continuity

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Figure 12

Step stress profile for vibration testing

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Figure 13

Random vibration profile at varying g levels

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Figure 14

Phase shift of healthy package (test board A, SAC305)

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Figure 15

Repeatability of phase-shift measurement on pristine healthy packages (test board A, SAC alloy)

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Figure 16

Confidence value as a lead indicator of failure during a drop test (test board B, PBGA676)

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Figure 17

Phase shift as a lead indicator of failure (test board B, PBGA676, 127 kHz)

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Figure 18

Phase shift as a lead indicator of failure (test board B, PBGA676, 6 MHz)

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Figure 19

Degradation of confidence value during drop test (test board A, CBGA package U2, f = 6 MHz)

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Figure 20

Degradation of confidence value during drop test (test board A, f = 6 MHz)

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Figure 21

Degradation of confidence value during drop test of packages U4, U6, and U7 for all interconnects. U4 is shown with blue circles, U5 is shown with green squares, and U6 is shown with red crosses (test board A, 127 kHz).

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Figure 22

Degradation of confidence value during drop test of packages U4, U6, and U7 on a normalized scale for all interconnects. U4 is shown with blue circles, U5 is shown with green squares, and U6 is shown with red crosses (test board A, 6 MHz).

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Figure 23

Method for determining prognostic distance using a threshold value shown in red (at the 20% confidence value). Each trace is an individual package (test board A, SAC305 alloy).

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Figure 24

PDF of prognostic distance at varying frequencies (test board A, SAC alloy)

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Figure 25

Damaged SAC interconnect (test board A)

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Figure 26

Broken trace on eutectic SnPb interconnect (test board A)

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Figure 27

Cracks through high lead (HIPB) interconnect (test board A)

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Figure 28

Failed CCGA interconnect (test board A)

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