Fast statistical analysis of rare failure events for memory circuits in high-dimensional variation space

- Accurately estimating the rare failure rates for nanoscale memory circuits is a challenging task, especially when the variation space is high-dimensional. In this paper, we summarize two novel techniques to address this technical challenge. First, we describe a subset simulation (SUS) technique to estimate the rare failure rates for continuous performance metrics. The key idea of SUS is to express the rare failure probability of a given circuit as the product of several large conditional probabilities by introducing a number of intermediate failure events. These conditional probabilities can be efficiently estimated with a set of Markov chain Monte Carlo samples generated by a modified Metropolis algorithm. Second, to efficiently estimate the rare failure rates for discrete performance metrics, scaled-sigma sampling (SSS) can be used. SSS aims to generate random samples from a distorted probability distribution for which the standard deviation (i.e., sigma) is scaled up. Next, the fa...

• Type: Journal
• Year: 2015
• Where: ASPDAC
• Authors: Shupeng Sun, Xin Li 0001
• Keywords: ASPDAC 2015, Hardware