Predictive Die-Level Reliability-Yield Modeling for Deep Sub-micron Devices

Ooi, Melanie Po-Leen; Kuang, Ye Chow; Chan, Chris; Demidenko, Serge

doi:10.1109/delta.2008.105

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…[2] proposes the use of joint-count sta spatial randomness. First, the local yield calculated as in [15]. The local yield is the av passing devices in a 3x3 neighbourhood wafer.…”

Section: Proposed Algorithm For Defect Clusmentioning

confidence: 99%

Accurate defect cluster detection and localisation on fabricated semiconductor wafters using joint count statistics

Ooi

Kang

Tee

et al. 2009

2009 1st Asia Symposium on Quality Electronic Design

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It is widely observed in the industry th tend to occur in groups of systematic patte called defect clusters. There are many prop achieve cluster classification and recognitio degree of accuracy and limitations. Many o although powerful, generally do not act presence/absence of a cluster but simply se then attempts to calculate the validity of th they fail to be flexible and accurate becaus assume that the problem is singular: ide clusters, when in actuality, the problem o identification can be divided into three detection, segmentation and recognition. Thi the use of joint-count statistics to perform defect cluster detection. It is recommended t and recognition be performed after the det completed to a satisfactory level.

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“…[2] proposes the use of joint-count sta spatial randomness. First, the local yield calculated as in [15]. The local yield is the av passing devices in a 3x3 neighbourhood wafer.…”

Section: Proposed Algorithm For Defect Clusmentioning

confidence: 99%

Accurate defect cluster detection and localisation on fabricated semiconductor wafters using joint count statistics

Ooi

Kang

Tee

et al. 2009

2009 1st Asia Symposium on Quality Electronic Design

Self Cite

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“…Spatial algorithms are a family of outlier detection methods based on the fact that defects tend to cluster. In [ 8 , 9 ], an approach based on a die-level neighborhood predictive model to successfully screen latent defects is proposed. By combining data mining with a defect-cluster extraction schema, it has been observed from production data that failing dice with traceable causes tend to form clusters at the wafer level or hot spots at the wafer-lot level.…”

Section: Introductionmentioning

confidence: 99%

Improving Electronic Sensor Reliability by Robust Outlier Screening

Moreno-Lizaranzu

Cuesta

2013

Sensors

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Electronic sensors are widely used in different application areas, and in some of them, such as automotive or medical equipment, they must perform with an extremely low defect rate. Increasing reliability is paramount. Outlier detection algorithms are a key component in screening latent defects and decreasing the number of customer quality incidents (CQIs). This paper focuses on new spatial algorithms (Good Die in a Bad Cluster with Statistical Bins (GDBC SB) and Bad Bin in a Bad Cluster (BBBC)) and an advanced outlier screening method, called Robust Dynamic Part Averaging Testing (RDPAT), as well as two practical improvements, which significantly enhance existing algorithms. Those methods have been used in production in Freescale® Semiconductor probe factories around the world for several years. Moreover, a study was conducted with production data of 289,080 dice with 26 CQIs to determine and compare the efficiency and effectiveness of all these algorithms in identifying CQIs.

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