Dynamic IR-Drop Prediction of At-Speed Two-Vector Tests Using Machine Learning
Journal
2024 International VLSI Symposium on Technology, Systems and Applications (VLSI TSA)
Part Of
2024 International VLSI Symposium on Technology, Systems and Applications, VLSI TSA 2024 - Proceedings 2024
ISBN (of the container)
979-835036034-9
Date Issued
2024-04-22
Author(s)
Yu-Tsung Wu
Zhe-Jia Liang
Chao-Ho Hsieh
Yun-Feng Yang
Yung-Jen Lee
Norman Chang
Ying-Shiun Li
Lang Lin
Abstract
Excessive dynamic IR-drop in VLSI testing causes timing violations, which leads to test failure. The dynamic IR-drop becomes a more serious problem in at-speed two-vector tests than that in stuck-at tests due to the at-speed clock. However, we need Machine Learning methods to speed up the analysis because of the long runtime of dynamic IR-drop analysis. In this paper, we propose two new methods to predict dynamic IR-drop of at-speed two-vector tests. One uses two models for the first capture cycle and the second capture cycle, respectively. The other one combines features of two capture cycles. Also, we propose spaced-window features and time-sliced features to improve prediction accuracy. Our mean absolute error for the worst dynamic IR-drop prediction is S.230m V, which is less than 0.6 % of the supply voltage. Our experiment results show at least a 12.6X speed-up ratio compared to a commercial tool. With our technique, we can identify two-vector tests which have excessive IR-drop in a short time to prevent yield loss.
Event(s)
2024 International VLSI Symposium on Technology, Systems and Applications, VLSI TSA 2024
Publisher
IEEE
Type
conference paper