Failure probability estimation with differently sized reference products for semiconductor burn‐in studies

Kurz, Daniel; Lewitschnig, Horst; Pilz, Jürgen

doi:10.1002/asmb.2100

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…These products are called follower products. The early life failure probability of the follower products scales with the die size (see, e.g., Kurz et al 22,23 ). We call this effect area scaling.…”

Section: Bi Time Reduction For Follower Productsmentioning

confidence: 99%

Flexible time reduction method for burn‐in of high‐quality products

Kurz

Lewitschnig

Pilz

2021

Quality & Reliability Eng

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Burn-in is an effective method to screen out early failures of electronic devices. Typically, this is achieved by operating the devices under accelerated stress conditions. This paper focuses on a burn-in concept where a random sample of devices is drawn out of the running production, put to burn-in, and investigated for early failures. This procedure is called burn-in study. In parallel, as long as the burn-in study is ongoing, all other produced devices are subjected to burn-in screening. In this article, new flexible sampling plans for burn-in studies are introduced. These are based on the progress of these studies and defined quality targets. Furthermore, these sampling plans enable fast burn-in time reductions and various time reduction strategies. From a statistical point of view, this requires to combine the proportion of early failures in a population with their lifetime distribution function. The new model is illustrated by case studies and simulations. It contributes to burn-in cost reductions, while controlling quality levels at the same time. K E Y W O R D Sbinomial distribution, burn-in, consumer's risk, lifetime distribution, sampling plan, zero defects INTRODUCTIONElectronic devices typically have an increased failure rate at the beginning of their lifetime which decreases over time. This phase is called early life. The burn-in (BI) is a state-of-the-art technique to screen out early failures of electronic devices. BI simulates the early life of the products under stress conditions for a certain time; this is the BI time. In doing so, early failures can be detected prior to delivery. Instructive overviews about BI are provided by Block and Savits, 1 Kececioglu and Sun, 2 and Kuo et al. 3 BI concepts for semiconductor devicesIn quality management, there are two major strategies to control quality levels:• 100% control and • random sampling.

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Section: Bi Time Reduction For Follower Productsmentioning

confidence: 99%

Flexible time reduction method for burn‐in of high‐quality products

Kurz

Lewitschnig

Pilz

2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

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“…Some of these models can find a joint probability distribution over a few parameters, but none are able to effectively take all the potential contributors into consideration. In this sense, artificial intelligence (AI) has been applied to conduct reliability analysis in such nonlinear and complex systems . Shi et al used an artificial neural network (ANN) model to predict the crack growth rate in Type 304 stainless steel.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of failure of rock bolts in underground coal mines using support vector machines

Jiang

Craig

Crosky

et al. 2017

Appl Stoch Models Bus & Ind

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In recent years, there has been an increasing incidence of failure of rock bolts due to stress corrosion cracking and localized corrosion attack in Australian underground coal mines. Unfortunately, prediction of the risk of failure from results obtained from laboratory testing is not necessarily reliable because it is difficult to properly simulate the mine environment. An alternative way of predicting failure is to apply machine learning methods to data obtained from underground mines. In this paper, support vector machines are built to predict failure of bolts in complex mine environments. Feature transformation and feature selection methods are applied to extract useful information from the original data. A dataset, which had continuous features and spatial data, was used to test the proposed model. The results showed that principal component analysis-based feature transformation provides reliable risk prediction.

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