Burn-in optimization under reliability and capacity restrictions

Chi, Dianwei; Kuo, Way

doi:10.1109/24.31104

Cited by 26 publications

(11 citation statements)

References 6 publications

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“…Kuo [12], and Chi and Kuo [8] provide a systematic analysis for electronic products, but they don't consider system and subsystem bum-in. Arsenault and Roberts [1], Bellcore [2], and U.S. Military Standard (MIL-STD-217D) apply "weight factors," which will be introduced later, to adjust component parameters under various conditions.…”

Section: A = Overall Compatibility Levelmentioning

confidence: 99%

Optimal Burn-in Simulation on Highly Integrated Circuit Systems

Chien

Kuo

1992

IIE Transactions

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Section: A = Overall Compatibility Levelmentioning

confidence: 99%

Optimal Burn-in Simulation on Highly Integrated Circuit Systems

Chien

Kuo

1992

IIE Transactions

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“…The use of a combined policy is sometimes more effective than the use of a pure policy [13]. An example of combined policy is presented by Golmakani and Fattahipour [19], who aimed to determine the best period for inspection and replacement in the condition-based maintenance.…”

Section: The Contextmentioning

confidence: 99%

Section: The Contextmentioning

confidence: 99%

“…Perlstein et al[29] analysed the cost of the optimal duration of burn-in, the components of which were characterized by hybrid exponential distributions using Bayesian theory. Restrictions can be added, as demonstrated by Chi and Kuo [13], who proposed a model to minimise costs under two restrictions, reliability and capacity.The use of a combined policy is sometimes more effective than the use of a pure policy [13]. An example of combined policy is presented by Golmakani and Fattahipour [19], who aimed to determine the best period for inspection and replacement in the condition-based maintenance.…”

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confidence: 99%

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Multi-attribute Utility Theory analysis for burn-in processes combined with replacement

Cavalcante¹,

Nascimento²,

Lopes³

2016

EiN

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Article citation info:1. Introduction The contextThe search for highly reliable systems has been intensifying. Such systems can be identified by implementing practices that reduce losses due to failures. Thus, maintenance plays a key role in organizations because it can significantly contribute to reducing both costs and failures, irrespective of the timing of failures during the useful life of the component. Preventive maintenance takes effect when the use of a particular policy enables the reduction of potential failures. Because these policies widely influence the safety and economy of operations, they are very important to improve the performance and reduce the cost of any producing systems.Burn-in procedures consist of testing a new component for a given period before its active life in order to prevent early failures.Burn-in has been studied by several researchers, including Kuo and Kuo [24], who presented a review of the main aspects of the procedure. Furthermore, Block and Savits [2] provided optimization examples and criteria. Regarding the adopted criteria, Block et al.[3] balanced residual life and variation (conditional survival) in the criterion of burn-in via the residual coefficient of variation, whereas Baskin [1] analysed burn-in using the general law of reliability. Perlstein et al.[29] analysed the cost of the optimal duration of burn-in, the components of which were characterized by hybrid exponential distributions using Bayesian theory. Restrictions can be added, as demonstrated by Chi and Kuo [13], who proposed a model to minimise costs under two restrictions, reliability and capacity.The use of a combined policy is sometimes more effective than the use of a pure policy [13]. An example of combined policy is presented by Golmakani and Fattahipour [19], who aimed to determine the best period for inspection and replacement in the condition-based maintenance. However, research studies involving combined policies of burn-in with replacement are rare, and the ones that stand out are those by Jiang and Jardine [21], Canfield [5], Thangaraj and Rizwam [32], Drapella and Koznik [15].A heterogeneous population of components that are present early and later fail related to exclusives failure models demands that the policy is adaptable. Therefore, a pure policy or the use of only one procedure is not effective. The present contributionDecisions regarding the burn-in and maintenance decisions are of great interest to researchers and decision-makers, and finding alternatives that improve the performance of managed systems is a challenge.CAvAlCAnte CAv, do nAsCimento tG, lopes Rs. multi-attribute Utility theory analysis for burn-in processes combined with replacement. eksploatacja i niezawodnosc -maintenance and Reliability 2016; 18 (4): 599-605, http://dx.doi.org/10.17531/ein.2016.4.15 The vast majority of research studies addresses the problem of burn-in with a single objective function. Although the adoption of combined policies represents an advance, combined procedures sometimes do not provide imp...

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“…Due to the expense of test time, HTOL board, test socket, it is necessary to find an optimized stress condition to minimize the costs of HTOL testing. Similar problems as semiconductor burn-in stress condition decision-making have been addressed in [1], [2]. An SA-based optimization algorithm [3] is proposed to reduce the resources of HTOL testing.…”

Section: Introductionmentioning

confidence: 99%

IC HTOL test stress condition optimization

Peng

Chen²,

Kuo

et al.

19th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 2004. DFT 2004. Proceedings.

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HTOL (High Temperature Operation Life) test is used to determine the effects of bias and temperature stress conditions on solid-state devices over time. It simulates the devices' operating condition in an accelerated manner, and is primarily for device reliability evaluation. This paper addresses an SA (Simulated Annealing) method used for the HTOL test stress condition decision-making that is an optimization problem. The goal is to reduce the resources for the HTOL test, hardware or time, under reliability constraints. The theory of reliability statistic model and the SA algorithm are presented. In our optimization algorithm, we need to calculate the accurate HTOL stressed power for the next optimization loop since the Vs (Stressed Voltage) that is optimized will affect not only Afv (Voltage Acceleration Factor) but also Aft (Thermal Acceleration Factor). A curve-fitting algorithm is applied to get reasonable accelerated factors and reliability calculations. The model selection process and statistical analysis of fitted data by different models are also presented. Experimental results with different stress condition priorities and different user settings are given to demonstrate the effectiveness of our approach.

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Burn-in optimization under reliability and capacity restrictions

Cited by 26 publications

References 6 publications

Optimal Burn-in Simulation on Highly Integrated Circuit Systems

Optimal Burn-in Simulation on Highly Integrated Circuit Systems

Multi-attribute Utility Theory analysis for burn-in processes combined with replacement

IC HTOL test stress condition optimization

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