A Yield Model Incorporating Random and Systematic Yield Part I: Theory

Melzner, Hanno

doi:10.1109/asmc.2008.4529031

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Since the introduction of the Negative Binomial yield model, this work can be regarded as completed [5]. Both Poisson and negative binomial have been widely used due to simplicity and accuracy respectively.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

“…Both Poisson and negative binomial have been widely used due to simplicity and accuracy respectively. However, now some researches show that using Poisson model to predict yield is pessimistic while negative binominal model is optimistic in some situations [5,6].…”

Section: Introductionmentioning

confidence: 99%