6 research outputs found
Speed Binning Aware Design Methodology to Improve Profit under Parameter Variations
Abstract-Designin
Statistical static timing analysis considering process variations and crosstalk
Increasing relative semiconductor process variations are making the prediction of
realistic worst-case integrated circuit delay or sign-off yield more difficult. As process
geometries shrink, intra-die variations have become dominant and it is imperative to
model them to obtain accurate timing analysis results. In addition, intra-die process
variations are spatially correlated due to pattern dependencies in the manufacturing
process. Any statistical static timing analysis (SSTA) tool is incomplete without a model
for signal crosstalk, as critical path delays can increase or decrease depending on the
switching of capacitively coupled nets. The coupled signal timing in turn depends on the
process variations. This work describes an SSTA tool that models signal crosstalk and
spatial correlation in intra-die process variations, along with gradients and inter-die
variations
Uncertainty Quantification of the Responses of Transient Electromagnetic Disturbance Coupling to Transmission Lines Based on Stochastic Models
L'abstract è presente nell'allegato / the abstract is in the attachmen
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 example demonstrate that APEX can provide better accuracy than Monte Carlo simulation with 10 4 samples and achieve orders of magnitude more efficiency. We also show the error incurred by the popular Normal modeling assumption using standard IC technologies. 1