An efficient, fully nonlinear, variability-aware non-monte-carlo yield estimation procedure with applications to SRAM cells and ring oscillators

2008 ◽  
Author(s):  
Chenjie Gu ◽  
Jaijeet Roychowdhury
Keyword(s):  
Monte Carlo ◽  
Estimation Procedure ◽  
Ring Oscillators ◽  
Yield Estimation ◽  
Fully Nonlinear

Production Yield Estimation by the Metamodel Method With a Boundary-Focused Experiment Design

1997 ◽  
Author(s):  
Chinghsin Tu ◽  
Russell R. Barton
Keyword(s):  
Monte Carlo ◽  
Robust Design ◽  
Simulation Models ◽  
Experiment Design ◽  
Production Yield ◽  
Design Stage ◽  
Yield Estimation ◽  
Simulation Code ◽  
New Approach ◽  
A Priority

Abstract The need for yield estimation strategies in the design stage is a priority recognized by industry. Yield estimates can be employed to assess the manufacturability of a design, and allow for modification to produce a robust design. Therefore, low yield of products can be avoided and costs for manufacturing can be reduced. This paper presents an accurate and time-efficient yield estimation approach for use with simulation models. We use a metamodel-based method, which is time-efficient compared to crude Monte Carlo yield estimation using the original simulation code. The approach employs a boundary-focused experiment design, which overcomes the inaccuracy of yield estimates that can occur when using a metamodel method. The results of two examples demonstrate the effectiveness of this new approach.

scholarly journals Heteroskedasticity in One-Way Error Component Probit Models

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 35
Author(s):  
Richard Kouamé Moussa
Keyword(s):  
Monte Carlo ◽  
Robustness Analysis ◽  
Probit Model ◽  
Estimation Procedure ◽  
Error Component ◽  
Ratio Test ◽  
Probit Models ◽  
Power Of The Test ◽  
Individual Effects ◽  
High Degree

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.

scholarly journals Hybrid importance sampling Monte Carlo approach for yield estimation in circuit design

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Anuj K. Tyagi ◽  
Xavier Jonsson ◽  
Theo G. J. Beelen ◽  
Wil H. A. Schilders
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Circuit Design ◽  
Yield Estimation ◽  
Monte Carlo Approach

scholarly journals Modeling of yield estimation for DNA strand breaks based on Monte Carlo simulations of electron track structure in liquid water

2019 ◽  
Vol 126 (12) ◽  
pp. 124701 ◽  
Author(s):  
Yusuke Matsuya ◽  
Takeshi Kai ◽  
Yuji Yoshii ◽  
Yoshie Yachi ◽  
Shingo Naijo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Liquid Water ◽  
Dna Strand Breaks ◽  
Track Structure ◽  
Yield Estimation ◽  
Strand Breaks ◽  
Electron Track ◽  
Dna Strand
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