论文标题
EM-GAN：使用生成对抗网络进行多段互连的快速应力分析
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks
论文作者
论文摘要
在本文中，我们提出了使用生成对抗网络（GAN）的多段互连电气互连的快速瞬态静水压力分析（EM）失败评估。我们的工作利用了基于GAN的生成深神经网络的图像合成特征。通过部分微分方程建模的多段互连的应力评估可以看作是随时间变化的2D图像到图像问题，其中输入是与当前密度的多段互连拓扑，并且输出是给定的衰变时间的这些线段中的EM应力分布。基于此观察结果，我们使用许多自我生成的多段线和电线电流密度以及衰老时间（作为条件）的图像来训练条件GAN模型，以训练COMSOL模拟结果。研究并比较了不同的GAN超参数。所提出的算法称为{\ it em-gan}，可以在给定的衰老时间内迅速给出一般多段线树的准确应力分布，这对于全芯片快速EM失败评估很重要。我们的实验结果表明，与comsol仿真结果相比，EM-GAN显示了6.6 \％平均误差，并具有数量级加速。它还超过了基于最先进的EM分析求解器的8.3倍加速。
In this paper, we propose a fast transient hydrostatic stress analysis for electromigration (EM) failure assessment for multi-segment interconnects using generative adversarial networks (GANs). Our work leverages the image synthesis feature of GAN-based generative deep neural networks. The stress evaluation of multi-segment interconnects, modeled by partial differential equations, can be viewed as time-varying 2D-images-to-image problem where the input is the multi-segment interconnects topology with current densities and the output is the EM stress distribution in those wire segments at the given aging time. Based on this observation, we train conditional GAN model using the images of many self-generated multi-segment wires and wire current densities and aging time (as conditions) against the COMSOL simulation results. Different hyperparameters of GAN were studied and compared. The proposed algorithm, called {\it EM-GAN}, can quickly give accurate stress distribution of a general multi-segment wire tree for a given aging time, which is important for full-chip fast EM failure assessment. Our experimental results show that the EM-GAN shows 6.6\% averaged error compared to COMSOL simulation results with orders of magnitude speedup. It also delivers 8.3X speedup over state-of-the-art analytic based EM analysis solver.
