Asymptotic probability extraction for non-normal distributions of circuit performance

While process variations are becoming more significant with each new IC technology generation, they are often modeled via linear regression models so that the resulting performance variations can be captured via Normal distributions. Nonlinear (e.g. quadratic) response surface models can be utilized to capture larger scale process variations; however, such models result in non-Normal distributions for circuit performance which are difficult to capture since the distribution model is unknown. In this paper we propose an asymptotic probability extraction method, APEX, for estimating the unknown random distribution when using nonlinear response surface modeling. APEX first uses a novel binomial moment evaluation to efficiently compute the high order moments of the unknown distribution, and then applies moment matching to approximate the characteristic function of the random circuit performance by an efficient rational function. A simple statistical timing example and an analog circuit ex...

• Type: Conference
• Year: 2004
• Where: ICCAD
• Authors: Xin Li, Jiayong Le, Padmini Gopalakrishnan, Lawrence T. Pileggi
• Keywords: Circuit Example Demonstrate, Hardware, ICCAD 2004, Random Circuit Performance, Unknown Random Distribution