Research Papers

Feasibility of Printed Circuit Board-Integrated Vibration Sensors for Condition Monitoring of Electronic Systems

[+] Author and Article Information
Klas Brinkfeldt

Argongatan 30,
Mölndal SE-431 53, Sweden
e-mail: klas.brinkfeldt@ri.se

Göran Wetter, Andreas Lövberg, Per-Erik Tegehall, Dag Andersson

Argongatan 30,
Mölndal SE-431 53, Sweden

Jan Strandberg

Bredgatan 33,
Norrköping SE-602 21, Sweden

Johnny Goncalves

NOTE Norrtelje AB,
Vilhelm Mobergs gata 18,
Norrtälje SE-761 46, Sweden

Jonas Söderlund

NOTE Norrtelje AB,
Vilhelm Mobergs gata 18,
Norrtälje SE-761 46, Sweden

Mikael Kwarnmark

Cogra Pro AB,
Fabriksvägen 1,
Älvängen SE-446 37, Sweden

1Corresponding author.

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received October 31, 2018; final manuscript received March 20, 2019; published online May 24, 2019. Assoc. Editor: Benjamin Leever.

J. Electron. Packag 141(3), 031010 (May 24, 2019) (7 pages) Paper No: EP-18-1096; doi: 10.1115/1.4043479 History: Received October 31, 2018; Revised March 20, 2019

The increasing complexity of electronics in systems used in safety critical applications, such as self-driving vehicles, requires new methods to assure the hardware reliability of the electronic assemblies. Prognostics and health management (PHM) that uses a combination of data-driven and physics-of-failure models is a promising approach to avoid unexpected failures in the field. However, to enable PHM based partly on physics-of-failure models, sensor data that measure the relevant environment loads to which the electronics are subjected during its mission life are required. In this work, the feasibility to manufacture and use integrated sensors in the inner layers of a printed circuit board (PCB) as mission load indicators measuring impacts and vibrations has been investigated. A four-layered PCB was designed in which piezoelectric sensors based on polyvinylidenefluoride-co-trifluoroethylene (PVDF-TrFE) were printed on one of the laminate layers before the lamination process. Manufacturing of the PCB was followed by the assembly of components consisting of ball grid arrays (BGAs) and quad flat no-leads (QFN) packages in a standard production reflow soldering process. Tests to ensure that the functionality of the sensor material was unaffected by the soldering process were performed. Results showed a yield of approximately 30% of the sensors after the reflow soldering process. The yield was also dependent on sensor placement and possibly shape. Optimization of the sensor design and placement is expected to bring the yield to 50% or better. The sensors responded as expected to impact tests. Delamination areas were present in the test PCBs, which requires further investigation. The delamination does not seem to be due to the presence of embedded sensors alone but rather the result of a combination of several factors. The conclusion of this work is that it is feasible to embed piezoelectric sensors in the layers of a PCB.

Copyright © 2019 by ASME
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Pecht, M. , 2008, Prognostics and Health Management of Electronics, Wiley, London.
Lall, P. , Islam, N. , Rahim, K. , and Suhling, J. , 2004, “ Prognosis Methodologies for Health Management of Electronics and MEMS Packaging,” ASME Paper No. IMECE2004-62319.
Vichare, N. M. , and Pecht, M. G. , 2006, “ Prognostics and Health Management of Electronics,” IEEE Trans. Compon. Packag. Technol., 29(1), pp. 222–229. [CrossRef]
Goebel, K. , Saha, B. , Saxena, A. , Celaya, J. R. , and Christophersen, J. P. , 2008, “ Prognostics in Battery Health Management,” IEEE Instrum. Meas. Mag., 11(4), pp. 33–40. [CrossRef]
Sandborn, P. A. , and Wilkinson, C. , 2007, “ A Maintenance Planning and Business Case Development Model for the Application of Prognostics and Health Management (PHM) to Electronic Systems,” Microelectron. Reliab., 47(12), pp. 1889–1901. [CrossRef]
Lee, J. , Wu, F. , Zhao, W. , Ghaffari, M. , Liao, L. , and Siegel, D. , 2014, “ Prognostics and Health Management Design for Rotary Machinery Systems—Reviews, Methodology and Applications,” Mech. Syst. Signal Process., 42(1–2), pp. 314–334. [CrossRef]
Heng, A. , Zhang, S. , Tan, A. C. C. , and Mathew, J. , 2009, “ Rotating Machinery Prognostics: State of the Art, Challenges and Opportunities,” Mech. Syst. Signal Process., 23(3), pp. 724–739. [CrossRef]
Littles , J. W., Jr. , Morris, R. J. , Pettit, R. , Harmon, D. M. , Savage, M. F. , and Tulpule, S. , 2006, “ Materials and Structures Prognosis for Gas Turbine Engines,” ASME Paper No. GT2006-91203.
Ray, A. , and Tangirala, S. , 1996, “ Stochastic Modeling of Fatigue Crack Dynamics for on-Line Failure Prognostics,” IEEE Trans. Control Syst. Technol., 4(4), pp. 443–451. [CrossRef]
Ko, J. M. , and Ni, Y. Q. , 2005, “ Technology Developments in Structural Health Monitoring of Large-Scale Bridges,” Eng. Struct., 27(12), pp. 1715–1725. [CrossRef]
Pecht, M. , and Jaai, R. , 2010, “ A Prognostics and Health Management Roadmap for Information and Electronics-Rich Systems,” Microelectron. Reliab., 50(3), pp. 317–323. [CrossRef]
Suhling, J. C. , and Jaeger, R. C. , 2001, “ Silicon Piezoresistive Stress Sensors and Their Application in Electronic Packaging,” IEEE Sens. J., 1(1), pp. 14–30. [CrossRef]
Velusamy, S. , Huang, W. , Lach, J. , Stan, M. , and Skadron, K. , 2005, “ Monitoring Temperature in FPGA Based SoCs,” IEEE International Conference on Computer Design: VLSI in Computers and Processors, San Jose, CA, Oct. 2–5, pp. 634–637.
Vichare, N. , Rodgers, P. , Eveloy, V. , and Pecht, M. G. , 2004, “ In Situ Temperature Measurement of a Notebook computer—A Case Study in Health and Usage Monitoring of Electronics,” IEEE Trans. Device Mater. Reliab., 4(4), pp. 658–663. [CrossRef]
Kwon, D. , Azarian, M. H. , and Pecht, M. , 2015, “ Remaining-Life Prediction of Solder Joints Using RF Impedance Analysis and Gaussian Process Regression,” IEEE Trans. Compon., Packag. Manuf. Technol., 5(11), pp. 1602–1609. [CrossRef]
Sirohi, J. , and Chopra, I. , 2000, “ Fundamental Understanding of Piezoelectric Strain Sensors,” J. Intell. Mater. Syst. Struct., 11(4), pp. 246–257. [CrossRef]
Guzman, E. , Cugnoni, J. , and Gmur, T. , 2015, “ Monitoring of Composite Structures Using a Network of Integrated PVDF Film Transducers,” Smart Mater. Struct., 24(5), p. 055017.
Elvin, N. G. , Elvin, A. A. , and Spector, M. , 2001, “ A Self-Powered Mechanical Strain Energy Sensor,” Smart Mater. Struct., 10(2), pp. 293–299. [CrossRef]
Crawley, E. F. , and De Luis, J. , 1987, “ Use of Piezoelectric Actuators as Elements of Intelligent Structures,” AIAA J., 25(10), pp. 1373–1385. [CrossRef]
Anton, S. R. , and Sodano, H. A. , 2007, “ A Review of Power Harvesting Using Piezoelectric Materials (2003-2006),” Smart Mater. Struct., 16(3), pp. R1–R21. [CrossRef]
Ottman, G. K. , Hofmann, H. F. , Bhatt, A. C. , and Lesieutre, G. A. , 2002, “ Adaptive Piezoelectric Energy Harvesting Circuit for Wireless Remote Power Supply,” IEEE Trans. Power Electron., 17(5), pp. 669–676. [CrossRef]


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Fig. 1

Printed sensor structure layout

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Fig. 2

Design of the test PCB showing the position of the sensor areas (S1-S20) in relation to the layout of the mounted BGA (IC1 2) and QFN (IC3-6) components. K1-K6 are 20-pin connectors for sensor output and polarization and J1-J2 are polarization areas.

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Fig. 3

Temperature profile used in the reflow process. The temperature was measured during a run with a panel without components which may result in a slightly higher peak temperature.

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Fig. 4

The bare (a) and mounted (b) test PCBs

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Fig. 5

The impact rig used for the impact tests

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Fig. 6

The yield of sensors that responded to polarization, per sensor position, measured on 10 test PCBs. The “C” indicates that the sensor was placed under a BGA or QFN component. “O” indicate a square shaped sensor and “I” indicate a rectangular shaped sensor.

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Fig. 7

Sensor response plotted against time for the impact tests using: (a) 2.0 g, (b) 3.6 g, (c)8.4 g, and (d) 13.9 g steel marble

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Fig. 8

X-ray and SAM images showing sensor S12 on C8 ((a) and (b)) and C9 ((c) and (d)) test PCBs

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Fig. 9

(a) X-ray image of sensor S12, where the white line and arrows indicate section and view direction. Overview images from the cross section from test PCBs C8 (b) and C9 (c).

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Fig. 10

Close-up microscope images of sensor S12 on test PCB C9

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Fig. 11

(a) SAM image of test PCB C8 showing delamination areas to the right of the center BGA676 component. (b) Cross-sectional image showing the delamination area across sensors S19 and S20 on test PCB C8. (c) Close-up image of the delamination of sensor S20 on test PCB C8.



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