Stochastic Yield Analysis of Rare Failure Events in High-Dimensional Variation Space

Speaker: Xiao Shi
Affiliation: Ph.D. Candidate

Abstract:  Stochastic yield analysis exhibits increasing importance for circuit designs under process variations. For highly-replicated standard cells, the failure event for each individual component must be extremely rare in order to maintain sufficiently high yield rate. Existing yield analysis approaches work fine at low dimension, but less effective either when there is a large amount of circuit parameters, or when the failure samples are distributed in multiple regions.

In this dissertation, a series of high sigma analysis approaches have been proposed. First, we present an adaptive importance sampling (AIS) method with two different sampling strategies to iteratively search for failure regions. Moreover, two meta-model assisted approaches are proposed for high sigma analysis. The low-rank tensor approximation (LRTA) method formulate the meta-model in tensor space by representing a multi-way tensor into a finite sum of rank-one tensor. The polynomial degree of our LRTA model grows linearly with circuit dimension, which makes it especially promising for high-dimensional circuit problems. Finally, the meta-model based importance sampling (MIS) method utilizes Gaussian Process meta-model to construct quasi-optimal importance sampling distribution, and performs Markov Chain Monte Carlo (MCMC) simulation to generate new samples from the proposed distribution. Experiment results demonstrate that the proposed approaches are 2-3 orders faster than Monte Carlo, and more accurate than both academia solutions such as IS, HDIS, and industrial solutions such as mixture IS used by ProPlus Design Automation, Inc.

Biography:  Xiao Shi received the B.S. degree in Electrical Engineering Department from Southeast University, Nanjing, China, in 2012, and the M.S. degree in Electrical Engineering from the University of California, Los Angeles, where he is currently pursuing his Ph.D. degree. His research interests mainly focus on uncertainty-aware techniques for computer-aided design, including stochastic circuit simulation, yield estimation and optimization.

For more information, contact Prof. Lei He ()

Date(s) - Mar 05, 2020
10:30 am - 12:30 pm

E-IV Tesla Room #53-125
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095