Physics-of-failure (PoF) methodology for electronic reliability

Hendricks, Christopher; George, Elviz; Osterman, Michael; Pecht, Michael

doi:10.1016/b978-1-78242-221-1.00003-4

Cited by 12 publications

(6 citation statements)

References 54 publications

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“…Step In this algorithm, the random samples x1, x2, …, xl are lifetime samples TTF_D (1) , TTF_D (2) , …, TTF_D (l) of the device, acquired by MC sampling procedure in Section II. In the algorithm, a sequential update strategy was adopted, which can obtain 1-order to M-order MaxEnt PDFs.…”

Section: Algorithmmentioning

confidence: 99%

“…Reliability evaluations for semiconductor devices mostly have three kinds of approaches: empirical methods, testing methods and physics-of-failure (PoF) methods [1]. Among these, PoF methods are recognized as physics-based approaches that consider failure mechanisms and processes to predict the lifetime of semiconductor devices on their service conditions [2]. The notable advantage of using PoF methods is that it could describe potential failure mechanisms and evaluate the reliability during the design stage, so it could save the cost and time and is widely used in reliability prediction.…”

Section: Introductionmentioning

confidence: 99%

“…represents kth TTF sample due to unrelated mechanisms at ith point.Finally, l lifetime sampling data of the device TTF_D(1) , TTF_D(2) , …, TTF_D (l) can be obtained by applying competing failure rules to multiple points and unrelated mechanisms. The sampling data acquired by MC sampling procedure can be used to fit the lifetime distribution of the device.…”

mentioning

confidence: 99%

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Reliability Evaluation of Multi-Mechanism Failure for Semiconductor Devices Using Physics-of-Failure Technique and Maximum Entropy Principle

Wan

Wang

et al. 2020

IEEE Access

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The physics-of-failure (PoF) technique is a practical approach to evaluate the reliability of semiconductor devices. However, the PoF approaches are usually insufficient in dealing with multimechanism failure and fitting the Monte Carlo (MC) sampling data. In our study, we propose an improved reliability evaluation method based on PoF technique and maximum entropy (MaxEnt) principle. The PoF models are used to generate time-to-failure samples of the failure mechanisms. Cumulative damage rules and competing failure rules are adopted to deal with multi-point and multi-mechanism failure and generate lifetime samples of the device. And the lifetime samples are fitted by MaxEnt distributions through the proposed fitting algorithm. The numerical examples given in the paper indicate that the MaxEnt distributions can describe the samples well and have a competitive advantage in dealing with multi-peak samples. A case study about a semiconductor device with multi-mechanism failure is presented to explain the workflow of the proposed reliability evaluation approach. The results show that the proposed MaxEnt distributions can yield reliable reliability evaluation results compared with Weibull and Lognormal distributions in the multi-mechanism failure process.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability Evaluation of Multi-Mechanism Failure for Semiconductor Devices Using Physics-of-Failure Technique and Maximum Entropy Principle

Wan

Wang

et al. 2020

IEEE Access

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“…Statistical literature is plenty of different methods for predicting the failure probability of a product. However, many of them could be considered limited, in terms of their applicability, since they are usually based on laboratory experimental data, mainly related to electronic and/or electrical components physical failures (see refs 1–4 …”

Section: Introductionmentioning

confidence: 99%

“…However, many of them could be considered limited, in terms of their applicability, since they are usually based on laboratory experimental data, mainly related to electronic and/or electrical components physical failures (see refs. [1][2][3][4] ). These factors are rarely the cause for a technological product to be returned, due to field failures.…”

Section: Introductionmentioning

confidence: 99%

Some tools to predict failure probability on products recently launched on the market: A mobile phone case study

Montoya¹,

Jiménez-Hernández

2022

Quality & Reliability Eng

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Prediction about the failure probability of a product or system is essential in reliability engineering, where a decision‐making process is carried out on a regular basis. This paper proposes a novel index, that allows to predict the failure rate on products recently launched into the market; its calculation is based on sales information and failure reports. Maximum likelihood method is used to derive a mathematical expression for this index, and prediction intervals are obtained throughout a bootstrap approach. A good performance regarding the mean square error and coverage probability is observed in the simulation study included here. Monthly sales data and mobile phone failure reports, provided by the company in charge of the warranty policy process, were used to illustrate our proposal that emerged from an academic‐industry collaboration.

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Physics‐of‐Failure Approach toPHM

Cheng

Raghavan

et al. 2018

Prognostics and Health Management of Electronics

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Physics-of-failure (PoF) methodology for electronic reliability

Cited by 12 publications

References 54 publications

Reliability Evaluation of Multi-Mechanism Failure for Semiconductor Devices Using Physics-of-Failure Technique and Maximum Entropy Principle

Reliability Evaluation of Multi-Mechanism Failure for Semiconductor Devices Using Physics-of-Failure Technique and Maximum Entropy Principle

Some tools to predict failure probability on products recently launched on the market: A mobile phone case study

Physics‐of‐Failure Approach toPHM

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