Towards identification of latent defects: Yield mining using defect characteristic model and clustering

Ooi, Melanie Po-Leen; Chan, Chris; Lee, S-L.; Mohanan, Ajay Achath; Goh, Ling Ying; Kuang, Ye Chow

doi:10.1109/asmc.2009.5155982

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…It is found that defect density near wafer edge tends to be distributed randomly, while at the wafer center tends to be cluster. And cluster also has several distinct patterns, such as blob, edge, scratch, hat and so on [16].…”

Section: Proposed Yield Modelmentioning

confidence: 99%

Random Yield Model in Integrated Circuit Design

Chen

2013

AMM

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As feature dimension of integrated circuits (IC) come into nanometer nodes, yield problems caused by random defects get worse. Even with advanced process techniques, the yield could not achieve 100%. Accurate prediction of yield can point out the direction of process optimization, shorten the production cycle, reduce the production cost, and then increase profits. In this paper, some kinds of random defects which can influence random yield are summarized. Then some widely used yield models are outlined and the drawbacks of these models are analyzed. At last, an improved yield model is proposed with the combination of Poisson model and negative binominal model. This model which takes distributions of random defects into consideration is more flexible and accurate. It can be seen from the simulation results that comparing to those existing models, the improved model indeed has higher fidelity and more flexibility.

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Section: Proposed Yield Modelmentioning

confidence: 99%

Random Yield Model in Integrated Circuit Design

Chen

2013

AMM

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“…Similarly, [ 12 ] show a strong correlation between probe data and device reliability. In [ 13 ] is investigated the use of defect characteristic models as yield models to screen latent defects for wafers with defect clusters. They propose and utilize a yield mining framework that guides manufacturers in determining if their device has a spatial relationship between the probe defects and latent defects.…”

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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Research on Machining Allowance Distribution Optimization based on Processing Defect Risk

Dai

2016

Procedia CIRP

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Towards identification of latent defects: Yield mining using defect characteristic model and clustering

Cited by 5 publications

References 12 publications

Random Yield Model in Integrated Circuit Design

Random Yield Model in Integrated Circuit Design

Improving Electronic Sensor Reliability by Robust Outlier Screening

Research on Machining Allowance Distribution Optimization based on Processing Defect Risk

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