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.
Highlights
Variation is a significant and expensive problem
The structure of the paper is as follows: Section 2 introduces the proposed prediction framework for path delay variation based on Machine Learning (ML) from single corner and multi-corner condition perspectives
Under the assumptions mentioned above, all path delay without/with any local variations is measured by SPICE/fast MC simulation based on the generated paths
Summary
Variation is a significant and expensive problem. Accompanying the development of integrated circuits, the feature size of technology is getting smaller and smaller and will continue shrinking in the future. The On-Chip Variation (OCV) and the Advanced OCV (AOCV) [26] were introduced in the current timing analysis flow and provide sufficient accuracy and risk reduction for Integrated Circuits (IC) designs, but they compare to traditional best-case and worst-case methods, which means they are all corner-based methodology. They can only obtain the path delay variation on the given characterized corners. The structure of the paper is as follows: Section 2 introduces the proposed prediction framework for path delay variation based on Machine Learning (ML) from single corner and multi-corner condition perspectives.
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