Novel Prediction Framework for Path Delay Variation Based on Learning Method

Abstract

Path delay variation becomes a serious concern in advanced technology, especially for multi-corner conditions. Plenty of timing analysis methods have been proposed to solve the issue of path delay variation, but they mainly focus on every single corner and are based on a characterized timing library, which neglects the correlation among multiple corners, resulting in a high characterization effort for all required corners. Here, a novel prediction framework is proposed for path delay variation by employing a learning-based method using back propagation (BP) regression. It can be used to solve the issue of path delay variation prediction under a single corner. Moreover, for multi-corner conditions, the proposed framework can be further expanded to predict corners that are not included in the training set. Experimental results show that the proposed model outperforms the traditional Advanced On-Chip Variation (AOCV) method with 1.4X improvement for the prediction of path delay variation for single corners. Additionally, while predicting new corners, the maximum error is 4.59%, which is less than current state-of-the-art works.