  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.
