Characterization of the time-dependent reliability fallout as a function of yield for a 130nm SRAM device and application to optimize production burn-in

Forbes, K.R.; Schani, P.

doi:10.1109/relphy.2004.1315318

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…He also suggested the ratio was at least 1/100. Estimates for this ratio have expanded to a region between 1/100 and 1/1000 [5]. Simply put, for every 1000 quality defects (yield at age zero), you should expect between 1 and 10 reliability defects.…”

Section: K ¼ Reliability Defect Density=quality Defect Densitymentioning

confidence: 99%

Using a new bathtub curve to correlate quality and reliability

Roesch¹

2012

Microelectronics Reliability

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Section: K ¼ Reliability Defect Density=quality Defect Densitymentioning

confidence: 99%

Using a new bathtub curve to correlate quality and reliability

Roesch¹

2012

Microelectronics Reliability

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“…& also suggested the ratio was at kast 11100.' Estimates for this ratio have expanded to a region between 11100 to IllOD0 [9]. Simply put, for every IO00 Qualay Defects (Yiild at age zero), you should expect between 1 and 10 Reliability Defects …”

Section: Studying Yield and Reiiabillfy Relationships For Metal Defectsmentioning

confidence: 99%

Studying yield and reliability relationships for metal defects

Roesch¹,

Hamada²

2004

JEDEC (Formerly the GaAs REL Workshop) ROCS Workshop, 2004.

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Phone: (503) 615-9292 FAX: (503) 615-8903 EMAIL: broesch@qs.com t a d 2 L .e 3 E a -._ 2 Introduction:With few exceptions, the reliability investigations on GaAs circuits over the past two decades have focused on thermally accelerated wearout f a k e mechanisms.Regardless of the measured lifetime, there have been no wearout failures reportd during use of the circuits. Instead, customers do report measurable defect rates and early life failures that often match-up with ykkl fallout failure mechanisms. This study mmtigata a particular defect that not only corresponds with m y of the early life failures, but is also &que to compound semiconductor manufacturing. The defect is generalized as liftoff metal shorting. Purpose:The intent of this work b to provide information on:I) Classlfyii metal defects, 2)Measuring w h g e effects. and 3) Investigating relationshps between yield and reliability. History -Bathtub CurveThe reliability of semiconductors has been historically modeled with a traditional "bathtub cunre" [l]. This curve m a p the generic failure rate versus time. Because the faiire rates are highest during the early life and at wearout, the curve suggests three regions of failure rate:Early: This has often been referred to as the "infant" region. The rate b assumed to be caused by defects and constantly decreasing throughout the lifetime of the device. For this discussion we'll name thk the "extrinsic" region. It is the region of specific interest in this study.Random: Thk is the region attributed to a constant f a k e rate. No specif& failure mechanisms have been assochlted with this region, although "defects" have been blamed here as the underlying cause for "random events." Application anomalies, such as overstress or electrostatic damage txe somethnes attributed to this region. In this study, we fmd no evidence that this region exists for normal operation of semiconductors, so we will not include a ''flat bottom" on OUI bathtub. Wearout: This has been the focus of most r e W i studies. Failures are produced under extreme acceleration conditionsprimarily driven by temperature. Failure r a t a increase constantly throughout the useful life of the distribution, and then drop off after the populatim is depleted. In this study, wearont faikne distriiutions are apparent under accelerated condilions, and they are 0-7908-0105-1/00/$0.00 2004 JEDEC Eaw Random weavut Extrinsic shnilar to traditional capacitor Wetest results. Thk coukl be an important region to consider, but wearout is not seen within the expected useful life of an electronic device or even within a human lifehe.Figure 1. An Empirical Bathtub Curve. Each of the batbtub regions contain mdple failure mechanisms and their assockted f a k e distributions.For example, the wearout region would typiCaUy mctude diffusion, electromigratbn, t h d excursion fatigue, corrosion, or dielectric breakdown failure mechanbms. The extrinsic region might contain all the same mechanisms, but under the influence of various defects. Detection of enough defects could produ...

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“…However, these procedures can have impact on manufacturing and shorten wear out lifetime. [1][2][3][4][5] In general, a defect size distribution varies depending on process lines, process time, learning experience gained, and other variables. The defect size probability density function is defined by a power law for defects smaller than the critical size and by an inverse power law for defects larger than the critical size.…”

Section: Introductionmentioning

confidence: 99%

Consideration of Burn-In acceleration and effective screening procedure in latest System LSI

Wakai

Kobira

Egawa

2008

2008 Annual Reliability and Maintainability Symposium

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An effective procedure to determine the Burn-In acceleration factors for latest System LSI (Large Scale Integration) with 90nm and 65nm technology are discussed in this paper. The relationship among yield, defect density, and reliability, is well known and well documented for defect mechanisms. In particular, it is important to determine the suitable acceleration factors for temperature and voltage to estimate the exact Burn-In conditions needed to screen these defects. The approach in this paper is found to be useful for recent Cu-processes which are difficult to control from a defectivity standpoint. Performing an evaluation with test vehicles of from 130nm to 65nm technology, the following acceleration factors were obtained, Ea>0.9ev and γ (Gamma)>-5.85.In addition, it was determined that a lower defect density gave a lower Weibull shape parameter. As a result of failure analysis, it is found that the main failures in these technologies were caused by particles, and their Weibull shape parameter "β" was changed depending of the related defect density. These factors can be applied for an immature time period where the process and products have failure mechanisms dominated by defects. Thus, an effective Burn-In is possible with classification from the standpoint of defect density, even from a period of technology immaturity.

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Characterization of the time-dependent reliability fallout as a function of yield for a 130nm SRAM device and application to optimize production burn-in

Cited by 13 publications

References 10 publications

Using a new bathtub curve to correlate quality and reliability

Using a new bathtub curve to correlate quality and reliability

Studying yield and reliability relationships for metal defects

Consideration of Burn-In acceleration and effective screening procedure in latest System LSI

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