The　routing　stage　is　one　of　the　most　time-consuming　steps　in　System　on　Chip　(SoC)　physical　design.　For　large　designs,　it　can　take　days　of　effort　to　find　a　complete　routing　solution,　and　the　result　directly　affects　the　circuit　performance.　In　this　paper,　we　present　a　routing　strategy　that　decomposes　global　routing　into　three　stages,　with　different　objectives　associated　with　each　stage.　This　is　in　contrast　to　conventional　approaches,　which　usually　use　a　single　global　optimization　objective　for　driving　the　entire　process.　Furthermore,　we　propose　to　use　generative　adversarial　networks　(GAN)　to　predict　the　congestion　heatmap.　This　deep　learning　method　has　been　used　to　successfully　improve　image　recognition　results.　We　adapt　its　use　to　global　routing　by　converting　data　between　the　router　and　the　image-based　model.　This　model　needs　only　placement　and　netlist　information　as　input　to　make　the　forecast.　Our　GAN-based　congestion　estimator　produces　congestion　heatmaps　that　show　good　fidelity　with　actual　heatmaps　produced　by　state-of-the-art　global　routers.　Using　this　heatmap　along　with　our　modified　routing　flow,　we　achieve　comparable　global　routing　quality　in　terms　of　the　total　overflow　and　wirelength,　but　the　runtime　speedup　on　hard-to-route　designs　is　significant.　©　2019　IEEE.