Monte-carlo driven stochastic optimization framework for handling fabrication variability

Increasing effects of fabrication variability have inspired a growing interest in statistical techniques for design optimization. In this work, we propose a Monte-Carlo driven stochastic optimization framework that does not rely on the distribution of the varying parameters (unlike most other existing techniques). Stochastic techniques like successive sample mean optimization (SSMO) and stochastic decomposition present a strong framework for solving linear programming formulations in which the parameters behave as random variables. We consider binning-yield loss (BYL) as the optimization objective and show that we can get a provably optimal solution under a convex BYL function. We apply this framework for the MTCMOS sizing problem [Khandelwal, V., et al., 2005] using SSMO and stochastic decomposition techniques. The experimental results show that the solution obtained from stochastic decomposition based framework had 0% yield-loss, while the deterministic solution [Khandelwal, V., et al., 2005] had a 48% yield-loss.