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Approximating probability distribution of circuit performance function for parametric yield estimation using transferable belief model

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Science China(Information Sciences) 2013年第11期56卷 106-124页

作者： XU XiaoBin ZHOU DongHua JI YinDong WEN ChengLin School of Automation and Institute of Systems Science and Control Engineering Hangzhou Dianzi University Department of Automation and Tsinghua National Laboratory for Information Science and Technology Tsinghua University Research Institute of Information Technology (RIIT) Tsinghua University

This paper applies the transferable belief model(TBM)interpretation of the Dempster-Shafer theory of evidence to approximate distribution of circuit performance function for parametric yield *** input parameters of performance function as credal variables defned on a continuous frame of real numbers,the suggested approach constructs a random set-type evidence for these *** corresponding random set of the function output is obtained by extension principle of random *** the TBM framework,the random set of the function output in the credal state can be transformed to a pignistic state where it is represented by the pignistic cumulative *** an approximation to the actual cumulative distribution,it can be used to estimate yield according to circuit response *** advantage of the proposed method over Monte Carlo(MC)methods lies in its ability to implement just once simulation process to obtain an available approximate value of yield which has a deterministic estimation *** the same error,the new method needs less number of calculations than MC methods.A track circuit of high-speed railway and a numerical eight-dimensional quadratic function examples are included to demonstrate the efciency of this technique.

关键词： Dempster-Shafer theory of evidence Monte Carlo methods parametric yield random set transferable belief model

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An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

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Frontiers of Information Technology & Electronic Engineering 2016年第2期17卷 160-172页

作者： Xin LI Jin SUN Fu XIAO Jiang-shan TIAN Technology Innovation Center Jiangsu Academy of Safety Science and Technology Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications School of Computer Science and Engineering Nanjing University of Science and Technology

With shrinking technology,the increase in variability of process,voltage,and temperature(PVT) parameters significantly impacts the yield analysis and optimization for chip *** yield estimation algorithms have been limited to predicting either timing or power ***,neglecting the correlation between power and delay will result in significant yield *** of these approaches also suffer from high computational complexity and long *** suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic(CAA) and the adaptive weighted sum(AWS) *** power and timing yield are set as objective functions in this *** two objectives are optimized simultaneously to maintain the correlation between *** proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary *** a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function(CDF) ***,the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal *** results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.

关键词： Parameter variations parametric yield Multi-objective optimization Chebyshev affine Adaptive weighted sum

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