GPR-Based Framework for Statistical Analysis of Gate Delay under NBTI and Process Variation Effects

Abstract

With the aggressive scaling of feature size, Negative Bias Temperature Instability (NBTI) and Process Variation (PV) have become major issues for circuit reliability and yield. In this paper, we analyze the variation of gate delay by jointly considering NBTI and PV effects. Using Gaussian Process Regression (GPR) learning interface, a Statistical Gate Delay Extraction (SGDE) framework is proposed. Typical types of logic gates are simulated with commercial 28 nm technology for verifying the performance of SGDE in the experiment. Compared to the golden data, the results show that our proposed approach achieves minimal loss of accuracy with significant runtime speed-up.