In Situ Interconnect Failure Prediction Using Canaries - IEEE Xplore

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IEEE TRANSACTIONS ON DEVICE AND MATERIALS RELIABILITY, VOL. 14, NO. 3, SEPTEMBER 2014

In Situ Interconnect Failure Prediction Using Canaries Preeti Chauhan, Member, IEEE, Sony Mathew, Member, IEEE, Michael Osterman, Member, IEEE, and Michael Pecht, Fellow, IEEE

Abstract—A physics-of-failure-based canary approach for early identification of solder interconnect failures has been developed. The canary is composed of a resistance path formed by a near-zero ohm ceramic chip resistor soldered to pads designed to produce failure earlier than standard pad resistors, which are the target structures. The time to failure of the canary can be adjusted by adjusting the printed wiring board pad dimensions and, hence, the solder interconnect area. The developed canary approach is demonstrated through temperature cycling of the resistors. The pad width of a standard resistor is reduced by 80%, thereby reducing the interconnect life. The results from the temperature cycling experiment prove that the developed canary approach provides advanced warning of failures of the standard pad resistors. The FEA results suggest that there is a 78% increase in the strain range in the canary resistor, as compared with the standard resistor. The Engelmaier model, a physics-of-failure-based model for solder interconnect life estimation under thermal cycling, is modified to take the solder interconnect area into account. The model provides time to failure estimates for the canary and target structures. A comparison of the results from the Engelmaier model and temperature cycling experiment shows that the model provides a good estimate of time to failure of standard resistors and a conservative estimate of time to failure of the canary resistors. Index Terms—Canary, physics-of-failure (PoF), prognostic distance, solder interconnects, thermal cycling.

I. I NTRODUCTION

P

ROGNOSTICS is defined as “a process of assessing the extent of deviation or degradation of a product from its expected normal operating conditions, and then predicting the future reliability of the product” [1]. Prognostics for electronic products and systems has received increased attention due to its potential to provide early warning of impending failures, forecast maintenance as needed, and reduce life cycle costs. Methods for prognostic implementation in electronic products and systems include monitoring of precursors to failure, use of canary devices, and monitoring of environmental and usage conditions experienced by a product in its application environment [1]. Physics-of-failure (PoF) is one approach to implement prognostics that utilizes knowledge of a product’ s life cycle loading conditions, geometry, material properties, and failure mechanisms to estimate its remaining useful life. Operating environment factors, such as temperature, operating voltage, humidity, vibration, and the presence of corrosive Manuscript received March 25, 2011; accepted June 12, 2013. Date of publication May 22, 2014; date of current version September 2, 2014. The authors are with the Department of Mechanical Engineering, University of Maryland, College Park, MD 20742 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TDMR.2014.2326184

substances, can reduce the life expectancy of electronic devices. Due to the inherent uncertainties in operating environments, the lifetime of an electronic device in field conditions might be substantially different from the lifetime measured under the controlled and specified conditions in laboratories. In the absence of good quality reliability data, design engineers often adopt either a narrow or worst-case design approach. While a worstcase design approach generally results in the over-specification of reliability requirements, narrow design considerations fail to account for excursions outside the specified operating ranges that occur in the service life of the electronic components. These approaches do not provide insight on system health state or life expectancy. The use of canary devices is one approach to take the uncertainties in the operating environment of electronics into account. An integrated circuit (IC) or printed circuit board (PCB) in an electronic device can be equipped with a component that experiences the expected and unexpected loads encountered during the operating life of the equipment, but fails earlier than the target system. Such a component is called a canary. A PoF-based canary approach takes into account geometry, material properties, and failure mechanisms, in addition to the real operating environments in which the target component operates, to provide an advance warning of failure of the target components. In this paper, a PoF-based canary approach to predict the time to failure of solder interconnects under temperature cycling is presented. The approach is demonstrated on two sets of ceramic chip resistors: a set of standard resistors with a standard pad width and a set of canary resistors with the pad width reduced by 80%. The canary structure adopted in the study is discussed in Section II. Section III discusses the Engelmaier model for solder interconnect life assessment under thermal cycling. Section IV discusses the deficiencies of the Engelmaier model and its modification to develop the PoF-based canary model. The canary approach is demonstrated by thermal cycling experiments in Section V. The finite element analysis (FEA) model to compare the strain ranges in canary and standard resistor solder joints during thermal cycling is discussed in Section VI, followed by the summary and conclusions in Section VII. II. P O F-BASED C ANARY A PPROACH FOR P REDICTING THE S OLDER I NTERCONNECT L IFE U NDER T HERMAL C YCLING Canaries are prognostic devices that are integrated into electronic assemblies to monitor the degradation of electronic

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CHAUHAN et al.: INTERCONNECT FAILURE PREDICTION USING CANARIES

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standard (0.132 inches) and canary (0.025 inches) pads. The canary and standard pad resistors are assembled using eutectic tin-lead (SnPb) solder. III. E NGELMAIER M ODEL FOR S OLDER J OINTS

Fig. 1.

Schematic of cross-sectional view of resistor assembly.

The Engelmaier model is a PoF model that estimates the time to failure of solder interconnects under thermal cycling [10]. The present canary approach is based on the Engelmaier model for leadless components. The Engelmaier model for leadless chip carriers is given as follows: 1c 1 F Ld ΔαΔTe Nf = 2 2εf h

Fig. 2.

(1)

(a) Standard pad resistor, and (b) Canary resistor.

components and predict their failure. A canary is designed to degrade by the same mechanism that leads to failure in the target device and fail before the target device. The time to failure difference between a canary and the target device is reported as the prognostic distance (PD), defined as “a measure of how long before system failure the prognostic structures or prognostic cell is expected to indicate failure” [1]. The failure times of the canary can be correlated with those of the target device using PD. Several studies [2]–[9] have utilized the canary approach to implement prognostics in electronics. For example, Goodman et al. [2] developed a prognostic cell (canary) to monitor the time-dependent dielectric breakdown and degradation of an IC. A feedback module was designed into the prognostic cell to remove power from the stressing circuit when the prognostic cell triggered in order to prevent the current drain and increased power dissipation if an oxide failure occurs. However, their approach includes additional circuitry to monitor the degradation of the target device. This paper demonstrates a simple solder interconnect canary that can be implemented with relatively low real estate using a ceramic chip resistor on a printed circuit board (FR4). Chip resistors are a common surface mount technology component with a high coefficient of thermal expansion mismatch with the substrate. The reliability of solder interconnects in such components is a concern for devices used in environments with high temperature excursions, such as those found in automotive and aerospace applications. In these applications, thermal fatigue is one of the primary failure mechanisms of solder interconnect failure. Our canary approach is demonstrated for monitoring the degradation of near-zero standard pad ceramic chip resistors under thermal cycling loading. Since the thermal cycling reliability of the solder interconnects depends on the solder interconnect area, a canary can be developed by reducing the solder interconnect area. The canary used in this study is a resistor assembled on a reduced width pad and is used to predict the time to failure of a resistor formed on a standard width pad. Fig. 1 shows a cross-section of a typical resistor assembly. The 2512 type resistors are mounted on copper pads on a printed circuit board (PCB) using solder interconnects. Fig. 2 shows the two configurations of resistors on the test vehicle:

where Nf is the mean cycles to failure, εf is the fatigue ductility coefficient (which is 0.325 for eutectic tin-lead solder), h is the solder joint height, F is the empirical “non-ideal” factor accounting for second-order effects, Ld is the distance from the neutral axis, Δα is the absolute difference in the coefficients of thermal expansion between the component and substrate, and ΔTe is the equivalent ΔT given by αs ΔTs − αc ΔTc (2) ΔTe = Δα where the subscript s denotes the substrate and the subscript c denotes the component, and c is the fatigue ductility exponent given by 360 c = −0.422 − 6∗ 10−4 Tsj + 1.74∗ 10−2 ln 1 + (3) td where Tsj is the mean cyclic solder joint temperature in Kelvin (the average of the minimum and maximum temperatures, ◦ C), and td is the dwell time in minutes. The Engelmaier model for leaded components is given by 1c 1 F K(LD ΔαΔTe )2 (4) Nf = 2 2εf (200 psi)Ah where K is the diagonal flexural stiffness of the unconstrained component lead determined by strain energy methods or finiteelement analysis, and A is the area of the solder joint. A noted issue with the Engelmaier model for leadless components is that it does not take into account the solder interconnect area ((1)) [11]. With a reduction in solder interconnect area, the time to failure of the component decreases. In order to account for solder interconnect area, the Engelmaier model for leadless part should be modified. IV. M ODIFIED E NGELMAIER M ODEL FOR L EADLESS C OMPONENTS The Engelmaier model is modified to take into account solder joint area by multiplying the strain range equation by an area factor A2 /A1 , where A2 is the area of a standard solder interconnect and A1 is the area of a canary solder interconnect.

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TABLE I TTF E STIMATIONS BY THE M ODIFIED E NGELMAIER M ODEL FOR T HREE C ASES OF NARROW PAD W IDTHS

The subscript M in (5) and (6) denotes the modified model. Under the modified formulation, the area effect is taken to be the same as the one Engelmaier defined for leaded parts. The resulting modified model is given by LD ΔαΔT A2 h1 A1 1c 1 Δγa = . 2 2εf

ΔγM =

NM

Fig. 3.

Test board mounted with standard and narrow pad resistors.

Fig. 4.

Resistance signature under thermal cycling.

(5)

(6)

Equation (6) gives the mean time to failure (MTTF), which is the time to failure for 50% of the test population. In order to compare the results from the Engelmaier model with our experimental results, the MTTF was extrapolated to the time to failure (TTF) of 63.2% of the population (η) by the following equation [12], [13]: 1c

1 ln(1 − 0.01x) β 1 ΔγM (7) NM (x%) = 2 2εf ln 0.5 where x = 63.2 and β is assumed to be 5. The prognostic distance is given by the difference between the TTFs of the standard and canary resistors. Here, the height of the solder and length of pad are constant. Different prognostic distances can be obtained by varying the width of the solder interconnects. Three cases of canary resistors with varying pad widths are shown in Table I: 0.025, 0.03, and 0.04 inches. The TTF data obtained by the modified Engelmaier model for the resistors assembled with the three pad widths are shown. The case marked with an asterisk (∗), with a pad width of 0.025 inches, is verified by a thermal cycling experiment (Section V). V. D EMONSTRATION OF THE C ANARY A PPROACH The canary approach was demonstrated by developing a canary resistor to monitor the degradation of standard pad resistors under thermal cycling loading. The experimental results of time to failure and prognostic distance were then compared with those obtained by our canary approach based on the Engelmaier model. A. Test Vehicle The test vehicle consisted of 2512 resistors mounted on an FR4 printed circuit board (Fig. 3). The test board consisted of 40 resistors assembled with SnPb solder. Twenty-four resistors

on the test board were canary resistors and the remaining sixteen were standard resistors. The board was tested until all of the resistors failed. B. Temperature Cycling Test The test board was subjected to preconditioning at 100 ◦ C for 24 hours in a thermal chamber and then subjected to thermal cycling from −55 ◦ C to 125 ◦ C (ramp rate of 10 ◦ C/min with a dwell time of 15 min at both the upper and lower ends). The cycle time was 72 minutes. Resistance monitoring was carried out using an Agilent 34980A multi-switch data acquisition system. The resistance data were acquired every 2 minutes. C. Results The failure criterion for the resistors was based on IPC-SM785 [14] and was defined as the first interruption of electrical continuity that was confirmed by 9 additional interruptions within the next 10% of the cycles. Fig. 4 shows a resistance signature that was typical of the test specimens. The threshold resistance for failure was 300 ohms. In Fig. 4, the failure threshold of 300 ohms is marked by a dashed line. The individual resistance readings are denoted by the points. Readings at or above 800 ohms are marked at 800 ohms and indicate an open circuit. The TTF data of the canary and standard pad resistors were then analyzed using Weibull++ software. It was found that the given data best fit the Weibull 2-parameter (β and η) distribution. Fig. 5 shows the unreliability vs. cycles to failure plot


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of a Weibull distribution, can be stated as follows: L(β, η) ≥ χ2α;2 . −2 · ln η) L(β,

(10)

The function f (x, θ) is dependent on the distribution type. For a Weibull distribution, k = 2 and L is a function of β and ηˆ) is given as follows: η. L(β,

β

β−1 n xi − x β i ˆ ηˆ) = ˆ ηˆ) = η · L(β, f (xi ; β, ·e . η η i=1 i=1 (11) n

Fig. 5.

Time (cycles) to failure plots for SnPb aged at 100 ◦ C for 24 hours.

for the standard and canary resistors. β is the shape parameter of the Weibull distribution. β < 1 indicates a decreasing failure rate and early failures, β = 1 indicates a constant failure rate and a normal operational life, and β > 1 indicates an increasing failure rate with wear-out failures. The β values for the two sets were 6.4 and 7 for the canary and standard pad resistors, respectively. The similar β values for the two sets suggest that both had the same failure mechanism, which was thermal fatigue in this case. The scale parameter η represents the characteristic life of the components, which is the time to failure for 63.2% of the test population. The canary resistors had a lower η value than the standard resistors, with a prognostic distance of 1776 cycles to the target resistors attached on standard pad geometries. The confidence bounds (at 95%) for the two data sets are also shown in 6. It can be seen that there is no overlap between the failure times. The confidence bounds of the estimated β and η parameters were estimated using the likelihood ratio (LR) method [15], [16]. The confidence bounds for the likelihood ratio are based on the following equation: L(θ) ≥ χ2α;k (8) −2 · ln ˆ L(θ)

It should be noted that k = 2 for Weibull distribution parameters. The values of the parameters satisfying the likelihood ratio equation depend on the required confidence level. For two-sided confidence bounds, α = confidence level. Equation (11) can be rearranged as follows: ˆ ηˆ) · e L(β, η) = L(β,

−χ2 α;2 2

.

(12)

The unknown term, L(β, η) can be obtained by substitutˆ ηˆ) and χ2 in (12). Once the value ing the values of L(β, α;2 of L(β, η) is known, the values of the β and η parameters satisfying the likelihood ratio equation have to be found. The solution is an iterative process in which the values of β and η are varied simultaneously in such a way that (12) is satisfied. The range of β and η parameters satisfying the likelihood ratio equation can be represented graphically as a contour plot. For the present study, β = 7 and η = 2214 for standard resistors and β = 6.4 and η = 438 for canary resistors. The confidence level is chosen to be 95%; hence, α = 0.95 for twosided confidence bounds. The method to calculate the confidence bounds for β and η parameters is illustrated using the failure data on standard pad resistors. For standard pad resistors, ηˆ) can be computed as R = 16, β = 7, and η = 2214. L(β, follows: ˆ ηˆ) = L(β,

x 6 7 xi 7 i · · e−( 2214 ) 2214 2214 i=1 16

(13)

ˆ ηˆ) = 1.7818e − 50 L(β,

(14)

χ20.95;2 is calculated to be 5.99 and put into the equation below: where L(θ) is the likelihood function for the unknown pa is the likelihood function calculated at rameter vector θ, L(θ) and χ2 is the chi-squared statistic the estimated vector (θ) α;k with probability α and k degrees of freedom, and is also the number of quantities jointly estimated. The likelihood functions for the unknown parameters θ1 through θk for n independent observations x1 through xn are given by L(x1 , x2 , . . . .xn |θ1 , θ2 , . . . , θk ) = L =

R

f (xi ; θ1 , θ2 , . . . , θk )

i=1

(9) The maximum likelihood estimators of θ1 through θk are obtained by maximizing L in (9). Equation (8), in the context

L(β, η) = 1.7818e − 50 · e L(β, η) = 8.91553e−5 =

−5.99 2

n β i=1

η

(15) ·

xi η

β−1

xi

β

· e−( η ) . (16)

After obtaining the value of L(β, η), the β and η parameters satisfying the likelihood ratio equation are found by an iterative process in which the values are varied simultaneously in such a way that above equation is satisfied. The same procedure is followed for the canary resistor joint failure data to obtain the bounds on β and η. The results of the above analysis are shown in the contour plots in Fig. 6. The yaxis represents the β values and the x-axis shows the η values. It can be seen that the estimated values of the characteristic

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Fig. 7.

Half model of resistor with reduced solder interconnection. TABLE III M ATERIAL P ROPERTIES FOR S IMULATION

Fig. 6. Confidence bounds of the estimated Weibull parameters, β and η. TABLE II C OMPARISON OF P O F-BASED C ANARY A PPROACH W ITH E XPERIMENTAL R ESULTS

life (η) for the two pad width configurations have no overlap and are statistically different. The clear separation of times to failure (at 95% confidence) indicates that the prognostic distance between the canary and standard resistor is statistically significant. The experimental results are compared with those obtained from the PoF approach using the Engelmaier model. The comparison is summarized in Table II. The results indicated a 1.88X change in the modified Engelmaier-defined total strain range between the standard and canary resistors for this test, with an exponent of 2.55 that results in a 5X life expectancy acceleration. For other conditions, the difference between the canary and standard resistors will vary based on the cycle conditions. Therefore, the expected field condition needs to be considered when implementing this type of canary. VI. F INITE E LEMENT A NALYSIS A 3-D finite element analysis was conducted to compare the strain produced in the canary and standard resistors. The obtained strain range is a damage accumulation metric for solder interconnects and correlates with the solder interconnect fatigue life. The finite element models were generated using ANSYS software. Fig. 7 shows the half model of a surface mount resistor with 80% reduced pad area assembled on a PCB. The resistor geometry is 0.25 inches (length), 0.126 inches (width), and 0.022 inches (height). For simplicity, the resistor is modeled as a volume of alumina without any tin end termination. The solder material is SnPb and the solder pad on the PCB is plain copper. The solder was modeled as a visco-plastic material with creep (generalized Garofalo model). The PCB thickness is 0.06 inch. The CTE values of the

Fig. 8.

Solder volume with high strain values.

material were obtained through laboratory testing. The material properties used are listed in Table III. The test condition (−55 ◦ C to 125 ◦ C) was simulated. Each of the FE model was subjected to three cycles since the increment strain per cycle does not change. The simulation showed that the solder interconnect on the resistor with reduced pad area showed a higher strain than the interconnect on the resistor with standard pad area. Additionally, the solder underneath the resistor body showed higher strain values than the bulk solder in the fillet portion of the interconnection. For analysis, the strain in the solder volume beneath the resistor is considered as shown in Fig. 8. The strain range calculated is the difference between the average strain in the selected solder volume at a high temperature dwell and that at a low temperature dwell. The ratio between the strain ranges experienced by the solder interconnect of resistor with a reduced pad and the resistor with a standard pad is 1.78. This means that there is a 78% higher strain range in the solder interconnects with a reduced area. In


the modified strain range equation for the Engelmaier model, as shown in (5), the delta strain value is a function of the ratio of the solder pad areas (A2/A1). This ratio of the areas for resistors with a standard pad area and reduced pad area is 5. The modification of the Engelmaier model is more conservative than the FEA estimates. This result is also confirmed by results obtained from testing.

VII. C ONCLUSION A PoF-based canary approach for predicting the solder interconnect failure in ceramic chip resistors under thermal cycling was demonstrated. The developed canary approach was demonstrated by testing the standard and canary resistors. In the canary resistors, the pad width was decreased by 80%. The two resistor configurations assembled with SnPb solder were then tested under thermal cycling and the times to failure were recorded. The assumption is that the time to failure of solder interconnects can be adjusted by adjusting the ratio of the attachment areas of the canary and standard resistors. The Engelmaier model was modified to account for solder pad area. The experimental results were then compared with the results derived from the modified Engelmaier model. The Engelmaier model adopted in the canary approach has better agreement (4% error) with the standard pad interconnect areas than with the canary attachment areas. The TTF estimates for the canary resistors based on the modified Engelmaier model gives a conservative TTF. One reason for this could be the area factor in the adjusted PoF model and the assumed β value. The FEA results suggest that there is a 78% increase in the strain range in the canary resistor, as compared to the standard resistor. The modification of the Engelmaier model provides more conservative estimates than both FEA and experiments. It has been demonstrated that the canary approach for prognostics can be successfully implemented. The time to failure of a canary resistor can be used to predict the time to failure of standard resistors. The prognostic distance, which is the difference between the TTF of the canary and the TTF of the target system, provides information to allow the maintenance and logistics personnel to repair or update the system to increase the system availability.

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[4] J. H. Lau, “The roles of DNP (Distance to Neutral point) on solder joint reliability of area array assemblies,” Solder. Surf. Mount Technol., vol. 9, no. 2, pp. 58–60, 1997. [5] J. W. Simons and D. A. Shockey, “Prognostics modeling of solder interconnects in electronic components,” in Proc. IEEE Aerosp. Conf., 2006, pp. 1–6. [6] L. Nasser and M. Curtin, “Electronics reliability prognosis through material modeling and simulation,” in Proc. IEEE Aerosp. Conf., 2006, pp. 1–7. [7] N. Vichare and M. Pecht, “Prognostics and health management of electronics,” IEEE Trans. Compon. Packag. Technol., vol. 29, no. 1, pp. 222–229, Mar. 2006. [8] J. Gu and M. Pecht, “Prognostics and health management using physicsof-failure,” in Proc. 54th Annu. Rel. Maintainability Symp., 2008, pp. 481–487. [9] M. J. Cushing, D. E. Mortin, J. J. Stadterman, and A. Malhotra, “Comparison of electronics-reliability assessment approaches,” IEEE Trans. Rel., vol. 42, no. 4, pp. 542–546, Dec. 1993. [10] J. P. Clech, W. Engelmaier, R. W. Kotlowitz, and J. A. Augis, “Reliability figures of merit for surface-soldered leadless chip carriers compared to leaded packages,” IEEE Trans. Compon. Hybrids Manuf. Technol., vol. 12, no. 4, pp. 449–458, Dec. 1989. [11] P. Chauhan, M. Osterman, and M. Pecht, “Critical review of the Engelmaier model for solder joint creep fatigue reliability,” IEEE Trans. Compon. Packag. Technol., vol. 32, no. 3, pp. 693–700, Sep. 2009. [12] W. Engelmaier, “The use environments of electronic assemblies and their impact on surface mount solder attachment reliability,” IEEE Trans. Compon. Hybrids Manuf. Technol., vol. 13, no. 4, pp. 903–908, Dec. 1990. [13] W. Engelmaier, “Generic reliability figures of merit design tools for surface mount solder attachments,” IEEE Trans. Compon. Hybrids Manuf. Technol., vol. 16, no. 1, pp. 103–112, Feb. 1993. [14] “Guidelines for accelerated reliability testing of surface mount solder attachments,” Northbrook, IL, USA, IPC-SM-785, 1992. [15] D. R. Thoman, J. B. Lee, and E. A. Charles, “Maximum likelihood estimation, exact confidence intervals for reliability, tolerance limits in the Weibull distribution,” Technometrics, vol. 12, no. 2, pp. 363–371, May 1970. [16] last accessed on 05-19-2014. [Online]. Available: http://www.weibull. com/hotwire/issue42/relbasics42.htm

Preeti Chauhan (M’14) received the B.Eng. degree in mechanical engineering from Madhav Institute of Technology and Science, Gwalior, India, in 2006 and the Ph.D. degree in mechanical engineering from the University of Maryland, College Park, MD, USA, in 2012. She is currently with the Department of Mechanical Engineering, University of Maryland. Her research interests include the reliability evaluation of lead-free solder interconnects in electronic devices. Dr. Chauhan is a member of SMTA.

ACKNOWLEDGMENT The authors would like to thank the more than 100 companies and organizations that support research activities at the Center for Advanced Life Cycle Engineering at the University of Maryland. R EFERENCES [1] M. Pecht, Prognostics and Health Management of Electronics. New York, NY, USA: Wiley-Interscience, 2008. [2] D. Goodman, B. Vermeire, J. Ralston-Good, and R. Graves, “A boardlevel prognostic monitor for MOSFET TDDB,” in Proc. IEEE Aerosp. Conf., 2006, pp. 1–6. [3] C. Y. Yin, H. Lu, M. Musallam, C. Bailey, and C. M. Johnson, “A physicsof-failure based prognostic method for power modules,” in Proc. 10th Electron. Packag. Technol. Conf., 2008, pp. 1190–1195.

Sony Mathew (M’14) is currently working toward the Ph.D. degree at the Center for Advanced Life Cycle Engineering (CALCE), Department of Mechanical Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. Previously, he managed the activities of the Prognostics and Health Management Group within CALCE. He developed, executed, and supervised research projects on prognostics of electronics and served as a liaison with CALCE’s industry and government partners. His research interests include reliability, and prognostics and health management of electronics. Dr. Mathew currently serves as the Vice Chair for the IEEE PHM standards (P1856) committee. He is a member of IMAPS, SMTA, and PHM Society.

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Michael Osterman (M’91) received the Ph.D. degree in mechanical engineering from the University of Maryland, College Park, MD, USA. He is currently the Director of the CALCE Electronic Products and Systems Consortium (EPSC) at the University of Maryland. He manages the information systems and oversees the development of software for CALCE EPSC. His research interests include virtual qualification techniques for electronic products, failure analysis for electronic systems, and information systems for electronics design. He has written various book chapters and numerous articles in the area of electronic packaging. Dr. Osterman is a member of ASME and SMTA.

Michael Pecht (F’92) received the M.S. degree in electrical engineering and the M.S. and Ph.D. degrees in engineering mechanics from the University of Wisconsin-Madison at Madison, WI, USA. He is the editor-in-chief of IEEE ACCESS, and he served as the Chief Editor for the IEEE T RANS ACTIONS ON R ELIABILITY for nine years, as the Chief Editor for Microelectronics Reliability for 16 years, and as an Associate Editor for the IEEE T RANSACTIONS ON C OMPONENTS AND PACKAG ING T ECHNOLOGY . He also served on the advisory board of IEEE Spectrum. He is the founder and Director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the world’ s leading electronics companies at more than US$6M/year. The CALCE received the NSF Innovation Award in 2009. He is currently a Chair Professor of mechanical engineering and a Professor of applied mathematics at the University of Maryland. He has written more than 20 books on product reliability, development, and use and supply chain management and over 600 technical articles. He consults for 22 international companies. Prof. Pecht is a world renowned expert in strategic planning, design, test, IP, and risk assessment of electronic products and systems. In 2013, he was awarded the Distinguished Achievement Award from the College of Engineering, University of Wisconsin-Madison, Madison, WI, USA. In 2010, he received the IEEE Exceptional Technical Achievement Award for his innovations in the area of prognostics and systems health management. In 2008, he was awarded the highest reliability honor, the IEEE Reliability Society’ s Lifetime Achievement Award. He is a Professional Engineer and a Fellow of ASME Fellow, SAE, and IMAPS. He has previously received the European Micro and Nano-Reliability Award for outstanding contributions to reliability research, 3M Research Award for electronics packaging, and the IMAPS William D. Ashman Memorial Achievement Award for his contributions in electronics analysis.