An efficient prediction framework for multi-parametric yield analysis under parameter variations

2016 ◽  
Vol 17 (12) ◽  
pp. 1344-1359 ◽  
Author(s):  
Xin Li ◽  
Jin Sun ◽  
Fu Xiao
Keyword(s):  
Yield Analysis ◽  
Parametric Yield ◽  
Parameter Variations ◽  
Efficient Prediction

Parametric yield analysis on FEM based microsystem designs

2007 ◽  
Author(s):  
Shyam Praveen Vudathu ◽  
Umamaheswara Reddy Vemulapati ◽  
Duane Boning ◽  
Vasudevarao Ramanolla ◽  
Rainer Laur ◽  
...  
Keyword(s):  
Yield Analysis ◽  
Parametric Yield

Genetic Algorithm-Based Multi-Objective Optimization for Statistical Yield Analysis Under Parameter Variations

2016 ◽  
Vol 26 (01) ◽  
pp. 1750009 ◽  
Author(s):  
Xin Li ◽  
Jin Sun
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Negative Correlation ◽  
Bayes Theorem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Parametric Yield ◽  
Markov Chain Method ◽  
Parameter Variations

Due to process scaling, variability in process, voltage, and temperature (PVT) parameters leads to a significant parametric yield loss, and thus impacts the optimization for circuit designs seriously. Previous parametric yield optimization algorithms are limited to optimizing either power yield or timing yield separately, without combining them together for simultaneous optimization. However, neglecting the negative correlation between the performance metrics, such as power and timing measurements, will bring on significant accuracy loss. This paper suggests an efficient multi-objective optimization framework based on Bayes’ theorem, Markov chain method, and an NSGA-II-based genetic algorithm. In the proposed framework, power and timing yields are considered as the optimization objectives to be optimized simultaneously, in order to maintain the negative correlation between power and timing metrics. First, the framework explicitly expresses both leakage current and gate delay in terms of the underlying PVT parameter variations. Then, parametric yields for both metrics are predicted by the computation of cumulative distribution function (CDF) based on Bayes’ theorem and Markov chain method. Finally, a NSGA-II-based genetic algorithm is suggested to solve power–timing optimization problem and generate well-distributed Pareto solutions. Experimental results demonstrate that the proposed multi-objective optimization procedure is able to provide the designer with guaranteed trade-off information between power and timing yields and give them the flexibility in choosing the most appropriate solution(s).

Parametric Yield Analysis and Optimization in Leakage Dominated Technologies

2007 ◽  
Vol 15 (6) ◽  
pp. 613-623 ◽  
Author(s):  
K. Agarwal ◽  
R. Rao ◽  
D. Sylvester ◽  
R. Brown
Keyword(s):  
Yield Analysis ◽  
Parametric Yield

Building an infrastructure for parametric yield analysis: concept and implementation of a DFM platform (Invited Paper)

2005 ◽  
Author(s):  
John Gookassian ◽  
Bob Pack ◽  
Mitch Heins ◽  
John Garcia ◽  
Hitendra Divecha ◽  
...  
Keyword(s):  
Yield Analysis ◽  
Parametric Yield ◽  
Analysis Concept
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