Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-chip Power Grid Network

With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for the first time, we propose a machine learning (ML) approach to obtain the EM-aware aging prediction of the PG network. We use neural network--based regression as our core ML technique to instantly predict the lifetime of a perturbed PG network. The performance and accuracy of the proposed model using neural network are compared with the well-known standard regression models. We also propose a new failure criterion based on which the EM-aging prediction is done. Potential EM-affected metal segments of the PG network is detected by using a logistic-regression--based classification ML technique. Experiments on different standard PG benchmarks show a significant speedup for our ML model compared to the state-of-the-art models. The predicted value of MTTF for different PG benchmarks using our approach is also better than some of the state-of-the-art MTTF prediction models and comparable to the other accurate models.