A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials
نویسندگان
چکیده
منابع مشابه
Monte-Carlo Simulations for Stochastic Optimization
1. Introduction Many important real-world problems contain stochastic elements and require optimization. Stochastic programming and simulation-based optimization are two approaches used to address this issue. We do not explicitly discuss other related areas including stochastic control, stochas-tic dynamic programming, and Markov decision processes. We consider a stochastic optimization problem...
متن کاملQuasi-monte Carlo Strategies for Stochastic Optimization
In this paper we discuss the issue of solving stochastic optimization problems using sampling methods. Numerical results have shown that using variance reduction techniques from statistics can result in significant improvements over Monte Carlo sampling in terms of the number of samples needed for convergence of the optimal objective value and optimal solution to a stochastic optimization probl...
متن کاملStochastic Logical Effort and Smart Monte Carlo for Timing Yield Estimation and Optimization
This paper presents novel techniques for timing yield optimization and for yield estimation in the presence of large statistical process variations in integrated circuits. The techniques are based on our generalization of the logical effort delay model to circuits with stochastic parameter variations. In the spirit of the standard logical effort formalism, the stochastic gate delay model we pro...
متن کاملNonlinear Stochastic Optimization by the Monte-Carlo Method
Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem...
متن کاملFast Monte Carlo Estimation of Timing Yield: Importance Sampling with Stochastic Logical Effort (ISLE)
In the nano era in integrated circuit fabrication technologies, the performance variability due to statistical process and circuit parameter variations is becoming more and more significant. Considerable effort has been expended in the EDA community during the past several years in trying to cope with the so-called statistical timing problem. Most of this effort has been aimed at generalizing t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM Transactions on Design Automation of Electronic Systems
سال: 2012
ISSN: 1084-4309,1557-7309
DOI: 10.1145/2071356.2071366