Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling

Hypersphere Sampling for Accelerating High-Dimension and Low-Failure Probability Circuit-Yield Analysis

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian dis...

A Technique for Efficient Evaluation of SRAM Timing Failure

This paper presents a technique to evaluate the timing variation of SRAM. Specifically, a method called loop flattening that reduces the evaluation of the timing statistics in the complex, highly structured circuit to that of a single chain of component circuits is justified. To then very quickly evaluate the timing delay of a single chain, a statistical method based on Importance Sampling augm...

Model Based Approach for Fault Detection and Prediction Using Particle Filters

Fault detection and failure prediction for nonlinear non-Gaussian systems is an important issue both from the economic and safety point of view. Most of the fault detection techniques assume the system model to be linear and the noise to be Gaussian. These linearization assumptions tend to suffer form poor detection and imprecise prediction. Also, they may lead to false alarms which would incur...

Nonlinear State Estimation by Evolution Strategies Based Particle Filters

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. By recognizing the similarities a n d th...

Efficient Failure Rate Prediction for SRAM Cells via Gibbs Sampling