Reliability prediction for microelectronic systems

O’Connor, P. D. T.; Head, Michael G.; Joy, Malcolm

doi:10.1016/0143-8174(85)90016-2

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…For different conditions the failure rates calculated at 55°C are given in Table II. The temperature dependent failure rate Z(T) is given by the following equation [2]:…”

Section: The Resultsmentioning

confidence: 99%

“…For different conditions the failure rates calculated at 55°C are given in Table II. The temperature dependent failure rate Z(T) is given by the following equation [2]: Z(T) = Ae -E a /KT (2) where A is a parameter taken as constant; E a is the thermal activation energy associated with the offending failure mechanism; K Boltzmann's constant = 8.65 × 10 -5 eV/Kelvin; T Absolute temperature in degrees Kelvin.…”

Section: The Resultsmentioning

confidence: 99%

“…The MIL-HDBK-217E [1] is expected to provide an adequate guide for this purpose, but definitely falls short in doing so. O'Connor et al [2] have convincingly described the limitations of this standard in predicting the failure rate of digital microelectronic systems. In a more recent publication O'Connor [3] has further highlighted the problem in reliability prediction using such standards.…”

Section: Introductionmentioning

confidence: 99%

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Effect of the product term on reliability prediction of integrated circuits

Kandasamy

1996

International Journal of Quality &Amp; Reliability Management

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Data for reliability prediction of integrated circuits is obtained either from well designed accelerated life tests or from MIL‐HDBK‐217E. Although a number of researchers have questioned the validity of prediction based on the data derived from this standard, it continues to remain the main source of failure data. Manufacturers normally specify chip reliability by a single failure rate, which is known to be incorrect. In a chosen reliability test, devices fail with different failure mechanisms characterized by their associated Arrhenius parameters. An overall failure rate for an integrated circuit is obtained by adding the individual failure rates. This amounts to ignoring the terms containing the product of probability of failure for different failure mechanisms. These terms can be ignored only if different failure mechanisms are mutually exclusive. Highlights this point, by using the published data and shows that a significant improvement is obtained by including the product terms. Demonstrates the idea of dropping the product term is sterile from theoretical as well as practical points of view and as such must be included. The chosen model and the example considered are for illustration only and the conclusion arrived at is not affected by the inherent uncertainties buried under the prediction process and associated tools. In this case it is equivalent to an improvement by 6 FITS. This approach is likely to have a meaningful impact on the economics of reliability prediction.

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“…For different conditions the failure rates calculated at 55°C are given in Table II. The temperature dependent failure rate Z(T) is given by the following equation [2]:…”

Section: The Resultsmentioning

confidence: 99%

Section: The Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of the product term on reliability prediction of integrated circuits

Kandasamy

1996

International Journal of Quality &Amp; Reliability Management

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Physics‐of‐Failure based Reliability Engineering

Quintero

Pecht

2013

Applied Reliability Engineering and Risk Analysis

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With increased use, complexity, and miniaturization of electronics in various applications, there is a need to improve the reliability of electronic products swiftly and cost-effectively. The shift from the use of traditional constant failure rate approaches to estimate the reliability of electronic products was enabled by physics-of-failure based reliability approaches. In a physics-offailure based reliability assessment, the failure mechanisms that degrade products and ultimately produce failures are identified, based on the hardware configuration and anticipated life-cycle profile. Physics-of-failure models associated with specific failure mechanisms are utilized to provide a statistical distribution of the time-to-failure for a particular failure mechanism and site. Physics-of-failure based reliability assessment is integrated into the product development process to aid in design-forreliability, stress test selection, product qualification, product screening, and prognostics and health management. This paper describes various steps involved in the physics-of-failure based reliability of electronic products and its integration into the product development process.

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Reliability prediction system based on the failure rate model for electronic components

Lee

2008

J Mech Sci Technol

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Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. Among the reliability prediction methods are the statistical analysis based method, the similarity analysis method based on an external failure rate database, and the method based on the physics-of-failure model. In this study, we developed a system by which the reliability of electronic components can be predicted by creating a system for the statistical analysis method of predicting reliability most easily. The failure rate models that were applied are MIL-HDBK-217F N2, PRISM, and Telcordia (Bellcore), and these were compared with the general purpose system in order to validate the effectiveness of the developed system. Being able to predict the reliability of electronic components from the stage of design, the system that we have developed is expected to contribute to enhancing the reliability of electronic components.

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Reliability prediction for microelectronic systems

Cited by 9 publications

References 3 publications

Effect of the product term on reliability prediction of integrated circuits

Effect of the product term on reliability prediction of integrated circuits

Physics‐of‐Failure based Reliability Engineering

Reliability prediction system based on the failure rate model for electronic components

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