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.

中文翻译：

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大规模片上电网增量设计中快速电迁移感知老化预测的机器学习方法

随着技术节点的进步，电迁移 (EM) 签核变得越来越困难，这需要相当长的时间来对芯片中的电网 (PG) 网络设计进行增量更改。传统的 Black 经验方程和 Blech 准则仍然用于 EM 评估，这是一个耗时的过程。在本文中，我们首次提出了一种机器学习（ML）方法来获得 PG 网络的 EM 感知老化预测。我们使用基于神经网络的回归作为我们的核心 ML 技术来即时预测受扰动的 PG 网络的生命周期。将所提出的使用神经网络的模型的性能和准确性与众所周知的标准回归模型进行了比较。我们还提出了一种新的失效准则，基于该准则进行 EM 老化预测。通过使用基于逻辑回归的分类 ML 技术检测 PG 网络中潜在的受 EM 影响的金属段。不同标准 PG 基准的实验表明，与最先进的模型相比，我们的 ML 模型显着加速。使用我们的方法对不同 PG 基准的 MTTF 预测值也优于一些最先进的 MTTF 预测模型，并且与其他准确模型相当。